Hoang Hai Nguyen

Profit-Optimal and Stability-Aware Load Curtailment in Smart Grids

A key feature of future smart grids is demand response. With the integration of a two-way communication infrastructure, a smart grid allows its operator to monitor the production and usage of power in real time. Upon detection of significant events, the operator may send requests to intelligent loads to curtail their power usage. The operator can use load curtailments reactively for adaptation to the loss of generation capacity (e.g., with unpredictable renewable energy sources), or proactively for profit maximization by avoiding the use of expensive energy sources during peak hours. In this paper, we optimize operator profits for the different cases of load curtailment, under various practical constraints including the physical properties of the power system, and different cost and valuation functions for heterogeneous generation units and loads, respectively. We also investigate the requirements imposed by different cases of the load curtailment on the cyber infrastructure. In particular, we evaluate how the delay of cyber control impacts the frequency stability of the power grid during the load curtailment phase.

Optimal false data injection attack against automatic generation control in power grids

This paper studies false data injection attacks against automatic generation control (AGC), a fundamental control system used in all power grids to maintain the grid frequency at a nominal value. Attacks on the sensor measurements for AGC can cause frequency excursion that triggers remedial actions such as disconnecting customer loads or generators, leading to blackouts and potentially costly equipment damage. We derive an attack impact model and analyze an optimal attack, consisting of a series of false data injections, that minimizes the remaining time until the onset of remedial actions, leaving the shortest time for the grid to counteract. We show that, based on eavesdropped sensor data and a few feasible-to-obtain system constants, the attacker can learn the attack impact model and achieve the optimal attack in practice. This paper provides essential understanding on the limits of physical impact of false data injections on power grids, and provides an analysis framework to guide the protection of sensor data links. Our analysis and algorithms are validated by experiments on a physical 16-bus power system testbed and extensive simulations based on a 37-bus power system model.

EnergyTrack: Sensor-Driven Energy Use Analysis System

Demand side management (DSM) has emerged as a promising way to balance the electrical grids demand and supply in an economical and environmentally friendly manner. For successful DSM, it is crucial to automate the analysis of building energy usage with respect to important factors that drive it, such as occupancy. In this paper, we present a sensor-driven energy use analysis system, EnergyTrack, that continuously analyzes, evaluates, and interprets building energy use in real-time. We develop an energy usage model in EnergyTrack that simultaneously considers device-specific energy consumption, occupancy changes, and occupant utility. We also design a low-cost occupancy estimation algorithm with a lightweight training requirement. The EnergyTrack testbed is implemented in a commercial building office space. Through this testbed, we demonstrate the performance of our occupancy estimation algorithm and the application of EnergyTrack in energy use analysis.

Modeling and Mitigating Impact of False Data Injection Attacks on Automatic Generation Control

This paper studies the impact of false data injection (FDI) attacks on automatic generation control (AGC), a fundamental control system used in all power grids to maintain the grid frequency at a nominal value. Attacks on the sensor measurements for AGC can cause frequency excursion that triggers remedial actions, such as disconnecting customer loads or generators, leading to blackouts, and potentially costly equipment damage. We derive an attack impact model and analyze an optimal attack, consisting of a series of FDIs that minimizes the remaining time until the onset of disruptive remedial actions, leaving the shortest time for the grid to counteract. We show that, based on eavesdropped sensor data and a few feasible-to-obtain system constants, the attacker can learn the attack impact model and achieve the optimal attack in practice. This paper provides essential understanding on the limits of physical impact of the FDIs on power grids, and provides an analysis framework to guide the protection of sensor data links. For countermeasures, we develop efficient algorithms to detect the attack, estimate which sensor data links are under attack, and mitigate attack impact. Our analysis and algorithms are validated by experiments on a physical 16-bus power system test bed and extensive simulations based on a 37-bus power system model.

Safety-Assured Collaborative Load Management in Smart Grids

When a power grid is overloaded, load shedding is a conventional way to combat the imbalance between supply and demand that may jeopardize the grids safety. However, disconnected customers may be excessively inconvenienced or even endangered. With the emergence of demand-response based on cyber-enabled smart meters and appliances, customers may participate in solving the imbalance by curtailing their demands collaboratively, such that no single customers will have to bear a disproportionate burden of reduced usage. However, compliance or commitment to curtailment requests by untrusted users is uncertain, which causes an important safety concern. This paper proposes a two-phase load management scheme that (i) gives customers a chance to curtail their demands and correct a grids undersupply when there are no immediate safety concerns, but (ii) falls back to conventional load shedding to ensure safety once the grid enters a vulnerable state. Extensive simulations based on a 37-bus electrical grid and traces of real electrical load demonstrate the effectiveness of this scheme. In particular, if customers are, as expected, sufficiently committed to the load curtailment, overloads can be resolved in real time by collaborative and graceful usage degradation among them, thereby avoiding unpleasant blackouts in existing practice.

Tracking appliance usage information using harmonic signature sensing

Real-time usage of individual electrical appliances is a key enabler of important advanced services for smart grids. With wide deployments of smart meters, there is a growing interest in using Non-Intrusive Load Monitoring (NILM) to acquire this information from the meter measurements. However, electrical signatures extracted from utility-side smart meters are often unreliable for NILM due to their large sampling intervals. This paper presents a new approach of using high-frequency current waveforms sampled periodically at a main branch to track reliably the on/off states of appliances in real-time. We develop an incremental training algorithm and a robust detection algorithm for the harmonic signatures, based on semi-supervised learning and a hidden Markov model, respectively. We evaluate the performance of the training and detection algorithms using simulations and a proof-of-concept testbed with five appliances. The simulation results show that our state detection algorithm is highly robust against noisy harmonic signatures - up to 16 times more robust than a baseline algorithm without the hidden Markov model. The experimental results show that the proposed algorithms can successfully learn most harmonic signatures using only 10% of label information. They can detect the on/off states with less than 4 % errors.

A Joint Data Compression and Encryption Approach for Wireless Energy Auditing Networks

Fine-grained real-time metering is a fundamental service of wireless energy auditing networks, where metering data is transmitted from embedded wireless power meters to gateways for centralized processing, storage, and forwarding. Due to limited meter capability and wireless bandwidth, the increasing sampling rates and network scales needed to support new energy auditing applications pose significant challenges to metering data fidelity and secrecy. This article exploits the compression and encryption properties of compressive sensing (CS) to design a joint data compression and encryption (JICE) approach that addresses these two challenges simultaneously. Compared with a conventional signal processing pipeline that compresses and encrypts data sequentially, JICE reduces computation and space complexities due to its simple design. It thus leaves more processor time and available buffer space for handling lossy wireless transmissions. Moreover, JICE features an adaptive reconfiguration mechanism that selects the signal representation basis of CS at runtime among several candidate bases to achieve the best fidelity of the recovered data at the gateways. This mechanism enables JICE to adapt to changing power consumption patterns. On a smart plug platform, we implemented JICE and several baseline approaches including downsampling, lossless compression, and the pipeline approach. Extensive testbed experiments show that JICE achieves higher data delivery ratios and lower recovery distortions under a range of realistic settings. In particular, at a meter sampling rate of 8 Hz, JICE increases the number of meters supported by a gateway by 50%, compared with the commonly used pipeline approach, while keeping a signal distortion rate lower than 5%.

JICE: Joint data compression and encryption for wireless energy auditing networks

Fine-grained real-time metering is a fundamental service of wireless energy auditing networks, where metering data is transmitted from embedded power meters to gateways for centralized processing, storage, and forwarding. Due to limited meter capability and wireless bandwidth, the increasing sampling rates and network scales needed to support new energy auditing applications pose significant challenges to metering data fidelity and secrecy. This paper exploits the compression and encryption properties of compressive sensing (CS) to design a joint data compression and encryption (JICE) approach that addresses these two challenges simultaneously. Compared with a conventional signal processing pipeline that compresses and encrypts data sequentially, JICE reduces computation and storage complexities due to its simple design. It thus leaves more processor time and available buffer space for handling lossy wireless transmissions. Moreover, JICE features a machine-learning-based reconfiguration mechanism that adapts its signal representation basis to changing power patterns autonomously. On a smart plug platform, we implemented JICE and several baseline approaches including downsampling, lossless compression, and the pipeline approach. Extensive testbed experiments show that JICE achieves higher data delivery ratios and lower recovery distortions under a range of realistic settings. In particular, JICE increases the number of meters supported by a gateway by 50%, compared with the pipeline approach, while keeping a distortion rate lower than 5%.

Scalable load disaggregation system using distributed electrical signature detection

We design and demonstrate a load disaggregation system that selectively uses high-frequency data from an add-on battery-powered mote hosting a current transducer (CT) sensor to complement low-frequency data from an infrastructure power meter. Our system learns, detects, and combines coarse electrical signatures from the infrastructure meter and finer signatures from the CT sensor to infer energy usage of targeted end-loads in real time. We demonstrate a proof-of-concept prototype of our system under a set of controlled experiments with various electrical loads.

Collaborative Load Management with Safety Assurance in Smart Grids

Load shedding can combat the overload of a power grid that may jeopardize the grid’s safety. However, disconnected customers may be excessively inconvenienced or even endangered. With the emergence of demand-response based on cyber-enabled smart meters and appliances, customers may participate in solving the overload by curtailing their demands collaboratively such that no single customers will have to bear a disproportionate burden of reduced usage. However, compliance or commitment to curtailment requests by untrusted users is uncertain, which causes an important safety concern. This article proposes a two-phase load management scheme that (i) gives customers a chance to curtail their demands and correct a grid’s overload when there are no immediate safety concerns but (ii) falls back to load shedding to ensure safety once the grid enters a vulnerable state. Extensive simulations based on a 37-bus electrical grid and traces of real electrical load demonstrate the effectiveness of this scheme. In particular, if customers are, as expected, sufficiently committed to the load curtailment, overloads can be resolved in real time by collaborative and graceful usage degradation among them, thereby avoiding unpleasant load shedding.