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Modern smart grid relies on various sensor measurements for its operational control. In a successful false data injection attack, the attacker manipulates the measurements from the grid sensors such that undetected errors are introduced into the estimates of the system parameters leading to catastrophic situations. This article proposes a novel perturbation based false data injection attack detection mechanism that utilizes inverter based distributed energy resources (DERs) to create low magnitude perturbation signal in the distribution system voltage that is inconsequential to the normal grid operation. Two voltage sensitivity analysis based algorithms are designed to identify the optimal set of DERs that can create the voltage perturbation signal of desired magnitude. An analytical method of voltage sensitivity analysis is used to compute the magnitude of voltage perturbation signal at each node in a computationally efficient manner. Then, a detection mechanism is developed that checks for the presence of the perturbation sequence in each sensor measurement. A sensor measurement is deemed authentic if the voltage perturbation signal is present in the data. In case of sensor malfunction or cyber-attack, the perturbation signal will not be present in the measurement data. Performance of the proposed attack detection mechanism is validated via simulation of the IEEE 69 bus test system.

Future smart power distribution system with multiple active consumers and aggregators exchanging real-time electricity pricing and electricity consumption information over a communication network is prone to cyberattack. The goal of this paper is to understand the impact of attacks on pricing/load signals on the physical grid within a transactive energy market framework. First, this paper models the interaction between real-time electricity price and total energy demand in the form of a discrete time nonlinear autonomous dynamical system. Second, equilibrium electricity price and energy demand associated with this coupled dynamical system is derived and conditions for bounded input bounded output (BIBO) stability are identified. Third, a BIBO stable algorithm to design real-time electricity pricing scheme from a techno-economic perspective is developed. Finally, the impact of various levels of false data injection (FDI) attack on price of electricity, demand, and distribution system voltage is investigated. The proposed model is analyzed using simulations on the IEEE 69-bus test system and the impact of FDI attack on both electricity price/demand and distribution grid voltage is quantified. This paper shows that impact of FDI attack on electricity prices is more severe than an attack on electricity demand.

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. First, this paper introduces a unique prospect theory-based consumer behavior model that captures wide range of consumers each with their individual preferences. Second, 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.

Variability in power generation at multiple consumer-owned renewable energy sources can cause random voltage fluctuations, which may result in voltage violations at multiple nodes in a power distribution system. Traditional methods of voltage control are ineffective to address fast voltage variations. New dynamic methods of voltage control using end-user devices can significantly benefit from the knowledge of the dominant influencer of voltage fluctuation (DIVF). This paper introduces information theoretic metrics to identify DIVF for every node in the power distribution system. A node can be DIVF due to its location in the distribution system and its capacity to generate and change its power. Use of traditional power flow algorithms to identify the DIVF is computationally complex, which limits their use in real-time applications. In this paper, information theoretic indicators of the DIVF are computed analytically in a computationally efficient manner, and their effectiveness to identify the DIVF is tested using the IEEE 69 node test system. From simulations, it is shown that differential entropy, Kullback–Leibler distance, and Frechet distance are effective indicators of the DIVF and can successfully identify the most influential nodes providing the foundation for intelligent voltage control.

From an operational and planning perspective, it is important to quantify the impact of increasing penetration of photovoltaics on the distribution system. Most existing impact assessment studies are scenario-based where derived results are scenario specific and not generalizable. Moreover, stochasticity in the temporal behavior of spatially distributed PVs requires a large number of scenarios that increase with the size of the network and the level of penetration. Therefore, we propose a new computationally efficient analytical framework of voltage sensitivity analysis that allows for stochastic analysis of voltage change due to random changes in PV generation. We first derive an analytical approximation for voltage change at any node of the network due to change in power at other nodes in an unbalanced distribution network. The quality of this approximation is reinforced via bounds on the approximation error. Then, we derive the probability distribution of voltage change at a certain node due to random changes in power injections/consumptions at multiple locations of the network. The accuracy of the proposed PVSA is illustrated using a modified version of the IEEE 37 bus test system. The proposed PVSA can serve as a powerful tool for proactive monitoring/control and ease the computational burden associated with perturbation based cybersecurity mechanisms., 10 pages, 9 figures, Preprint under review in IEEE Transactions on Power Systems

Large scale integration of distributed energy resources and electric vehicles in a transactive energy environment present new challenges in terms of voltage stability and fluctuations in a power distribution system. The impact of different level of DER/EV penetration on the voltages across the network is typically quantified through voltage sensitivity analyses. Existing methods of voltage sensitivity analysis are computationally expensive and prior efforts to develop analytical approximation lacks generality and have not been effectively validated. The objective of this work is to provide a new analytical method of voltage sensitivity analysis that has low computational cost and also allows for stochastic analysis of voltage change. This paper first derives an analytical approximation of change in voltage at a particular bus due to change in power consumption at other bus in a radial three phase unbalanced power distribution system. Then, the proposed method is shown to be valid for different load configurations, which demonstrates its generality. The results from our analytical approach is validated via classical load flow simulation of the test system based on IEEE 37 bus network. The proposed method is shown to have good accuracy, and computation complexity is of order O(1), compared to O(n3) in classical sensitivity analysis approaches., Comment: 5 pages, 3 figures, Preprint accepted in IEEE PES GM 2020 Conference

Smart grids are envisioned to accommodate high penetration of distributed photovoltaic (PV) generation, which may cause adverse grid impacts in terms of voltage violations. Therefore, PV Hosting capacity is being used as a planning tool to determine the maximum PV installation capacity that causes the first voltage violation and above which would require infrastructure upgrades. Traditional methods of Hosting capacity analysis are scenario based and computationally complex as they rely on iterative load flow algorithms that require investigating a large number of scenarios for accurate assessment of PV impacts. Therefore, this paper presents a computationally efficient analytical approach to compute the probability distribution of voltage change due to random behavior of randomly located multiple distributed PVs. The proposed approach is based on Spatio-temporal probabilistic voltage sensitivity analysis that exploits both spatial and temporal uncertainties associated with PV injections. Thereafter, the derived distribution is used to quantify voltage violations for various PV penetration levels and subsequently determine the hosting capacity of the system without the need to examine large number of scenarios. Results of the proposed framework are validated via conventional load flow based simulation approach on the IEEE 37 and IEEE 123 node test systems.

Active consumers will be integral to the future smart grid, scheduling their load and managing renewable generation in accordance with real-time pricing. The impact of spatially distributed renewable generation and active consumers on the distribution system must be quantified for operational planning. Most of the current studies are scenario-based and derived results are scenario specific. Determining the impact of spatially distributed active consumers with temporally variable behavior requires investigation of a large number of scenarios, which is computationally intractable using current iterative power flow algorithms. The objective of this work is to provide a new analytical method of voltage sensitivity analysis that allows for stochastic analysis of change in grid voltage due to change in consumer behavior (load and generation choices). This paper first derives an upper bound for change in voltage at a particular bus due to change in complex power at other buses in a radial distribution network. This bound is verified via simulations. Next, this bound is used to derive the probability distribution of voltage change at a bus due to random behavior of spatially distributed multiple active consumers. Results from the proposed probabilistic voltage sensitivity analysis is validated via traditional-scenario-based simulation of the IEEE 69 bus test system.

Electric vehicles (EVs) are becoming increasingly popular because of their low operating costs. However, the anticipated increase in EV usage presents opportunities as well as challenges fo...

Electric vehicles (EVs) are increasing in popularity because of their low operating cost and environmental friendly operation. The anticipated rise in EV usage, along with the increased use of renewable energy sources and smart storage devices for EV charging, presents opportunities as well as challenges. Time-varying electricity pricing and day-ahead power commitment add another dimension to this problem. In this research, we develop coordinated EV charging strategies for renewable energy powered charging stations in parking lots. This work maximizes the profits for parking lot operators by taking advantage of time-varying electricity pricing while satisfying system constraints. We propose a linear programming-based strategy for EV charging, and we specifically derive a centralized linear program that minimizes the charging cost for parking lot operator while satisfying customers’ demand in available time.

With abundant availability of electricity customers load data, and the growing trend toward smart distribution grid, there is a need for more efficient approaches to exploit the valuable customer load information from the high-resolution data collected from customers by automatic meter reading (AMR). New effective clustering methods such as affinity propagation are one of the ways to tackle this issue by improving load prediction techniques and devising efficient pricing schemes. In this paper, an affinity propagation (AP) algorithm is used to cluster customer load data and generate typical load profiles (TLP) for clusters. AP is a new algorithm and has no need to have a predefined number of clusters. Clustering results are compared with some traditional methods such as k-mean, k-medoid, and spectral clustering. Also, the AP results are evaluated by computing a range of well-known clustering performance indices.

Increased penetration levels of distributed variable renewable generation can cause random voltage fluctuations and violations at multiple nodes. Traditional methods of voltage control typically involve reactionary responses of capacitor banks, tap changers, and recently even smart inverters. But because of the lack of foresight in voltage violations, these controls are ineffective to completely mitigate the issue. Therefore, new methods of predicting voltage violations subject to random power injection changes in the distribution network are needed, which can be used to guide optimal and dynamic methods of voltage control. This work lays the foundation for such preemptive voltage monitoring and control by proposing an analytical and sensor data-driven voltage sensitivity analysis method. Driven by stochastic data and forecasts, the method can be used to develop probabilistic voltage sensitivities and consequently to predict system nodes with high likelihood of voltage limit violations. The effectiveness of this method is tested on IEEE 69-node distribution system integrated with distributed solar. The results demonstrate the proposed method's ability to successfully predict nodes with high probability of voltage violations for a specific time-series simulation. The results also demonstrate the ability to guide timely power injection control actions to mitigate future voltage violations.

Growing concerns about global warming, air pollution, and fossil fuel shortages have prompted the research and development of energy efficient electric vehicles (EVs). The United States government has a goal of putting 1 million EVs on the road by 2015. The anticipated increase in EV usage, along with the use of renewable energy sources for EV charging presents opportunities as well as technical hurdles. In this work, we propose coordinated EV charging strategies for commercial charging stations in parking lots. The focus of the research is on minimizing energy drawn from the grid while utilizing maximum energy from renewable energy resources in order to maximize benefits to parking lot owners. We propose an optimal control theory based strategy for EV charging. Specifically we derive a centralized iterative control approach in which the charging rates of EVs are optimized one at a time. Through analysis and simulations, we demonstrate that optimizing the charging rate of one vehicle at a time and repeating this process for all vehicles iteratively converges to the global optimum.