The Combined Effect of Photovoltaic and Electric Vehicle Penetration on Conservation Voltage Reduction in Distribution System

Global conditions over the past dozen years have led to an expanded appetite for renewable energy sources: The diminishing fossil fuel supply, the political instability of countries producing these fossil fuels, the ever-more destructive effects of global warming, and the lowering of costs for renewable energy technologies have made countries around the world reconsider their sources of energy. The proliferation of photovoltaic (PV) systems especially has surged dramatically with the decreasing initial costs for installation, and increasing government support in the form of renewable energy portfolios, feed-in-tariffs, tax incentives, etc. Furthermore, electric vehicles (EV) are also becoming widespread due to recent advances in battery and electric drive technologies, and the desperate need to reduce air pollution in urban areas. Meanwhile, electric utilities are always making an effort to run their system more efficiently by encouraging the use of energy-efficient appliances and customer participation in demand-side management programs. In an attempt to further reduce load demand; many utilities regulate the voltage along their distribution feeders in a particular way that is referred to as conservation voltage reduction (CVR). The key principle of CVR operation is that the ANSI standard voltage band between 114 and 126 volts can be compressed via regulation to the lower half (114–120) instead of the upper half (120–126), producing measurable energy savings at low cost and without harm to consumer appliances. As the penetration of distributed PV and EV charging station increases, this can dramatically change the conventional demand profile as PV system act as negative loads during the daylight hours, and EVs significantly increase load demand during charging. Consequently, traditional means of controlling the voltage by capacitor switching and voltage regulators can be improved in this “smart” grid era by adding a fleet of enabling devices including the smart PV inverter functionalities, such as Volt/VAR control, and intelligent EV charging schemes. This thesis explores how better energy conservation is achieved by CVR in a modern distribution system with advanced distributed PV systems inverters and EV loads. Then it summarizes computer simulations that are conducted on the IEEE 37 and IEEE 123 node test feeders using OpenDSS interfaced with MATLAB

Bridging Machine Learning for Smart Grid Applications

This dissertation proposes to develop, leverage, and apply machine learning algorithms on various smart grid applications including state estimation, false data injection attack detection, and reliability evaluation. The dissertation is divided into four parts as follows.. Part I: Power system state estimation (PSSE). The PSSE is commonly formulated as a weighted least-square (WLS) algorithm and solved using iterative methods such as Gauss-Newton methods. However, iterative methods have become more sensitive to system operating conditions than ever before due to the deployment of intermittent renewable energy sources, zero-emission technologies (e.g., electric vehicles), and demand response programs. Efficient approaches for PSSE are required to avoid pitfalls of the WLS-based PSSE computations for accurate prediction of operating conditions. The first part of this dissertation develops a data-driven real-time PSSE using a deep ensemble learning algorithm. In the proposed approach, the ensemble learning setup is formulated with dense residual neural networks as base-learners and a multivariate-linear regressor as a meta-learner. Historical measurements and states are utilized to train and test the model. The trained model can be used in real-time to estimate power system states (voltage magnitudes and phase angles) using real-time measurements. Most of current data-driven PSSE methods assume the availability of a complete set of measurements, which may not be the case in real power system data acquisition. This work adopts multivariate linear regression to forecast system states for instants of missing measurements to assist the proposed PSSE technique. Case studies are performed on various IEEE standard benchmark systems to validate the proposed approach. Part II: Cyber-attacks on Voltage Regulation. Several wired and wireless advanced communication technologies have been used for coordinated voltage regulation schemes in distribution systems. These technologies have been employed to both receive voltage measurements from field sensors and transmit control settings to voltage regulating devices (VRDs). Communication networks for voltage regulation can be susceptible to data falsification attacks, which can lead to voltage instability. In this context, an attacker can alter multiple field measurements in a coordinated manner to disturb voltage control algorithms. The second part of this dissertation develops a machine learning-based two-stage approach to detect, locate, and distinguish coordinated data falsification attacks on control systems of coordinated voltage regulation schemes in distribution systems with distributed generators. In the first stage (regression), historical voltage measurements along with current meteorological data (solar irradiance and ambient temperature) are provided to random forest regressor to forecast voltage magnitudes of a given current state. In the second stage, a logistic regression compares the forecasted voltage with the measured voltage (used to set VRDs) to detect, locate, and distinguish coordinated data falsification attacks in real-time. The proposed approach is validated through several case studies on a 240-node real distribution system (based in the USA) and the standard IEEE 123-node benchmark distribution system.Part III: Cyber-attacks on Distributed Generators. Part III of the dissertation proposes a deep learning-based multi-label classification approach to detect coordinated and simultaneously launched data falsification attacks on a large number of distributed generators (DGs). The proposed approach is developed to detect power output manipulation and falsification attacks on DGs including additive attacks, deductive attacks, and combination of additive and deductive attacks (attackers use the combination of additive and deductive attacks to camouflage their attacks). The proposed approach is demonstrated on several systems including the 240-node and IEEE 123-node distribution test system. Part IV: Composite System Reliability Evaluation. Traditional composite system reliability evaluation is computationally demanding and may become inapplicable to large integrated power grids due to the requirements of repetitively solving optimal power flow (OPF) for a large number of system states. Machine learning-based approaches have been used to avoid solving OPF in composite system reliability evaluation except in the training stage. However, current approaches have been utilized only to classify system states into success and failure states (i.e., up or down). In other words, they can be used to evaluate power system probability and frequency reliability indices, but they cannot be used to evaluate power and energy reliability indices unless OPF is solved for each failure state to determine minimum load curtailments. In the fourth part of this dissertation, a convolutional neural network (CNN)-based regression approach is proposed to determine the minimum amount of load curtailments of sampled states without solving OPF. Unavoidable load curtailments due to failures are then used to evaluate power and energy indices (e.g., expected demand not supplied) as well as to evaluate the probability and frequency indices. The proposed approach is applied on several systems including the IEEE Reliability Test System and Saskatchewan Power Corporation in Canada

Third Generation Gamma Camera SPECT System

Single Photon Emission Computed Tomography (SPECT) is a non-invasive imaging modality, frequently used in myocardial perfusion imaging. The biggest challenges facing the majority of clinical SPECT systems are low sensitivity, poor resolution, and the relatively high radiation dose to the patient. New generation systems (GE Discovery, DSPECT) dedicated to cardiac imaging improve sensitivity by a factor of 5-8. The purpose of this work is to investigate a new gamma camera design with 21 hemi-ellipsoid detectors each with a pinhole collimator for Cardiac SPECT for further improvement in sensitivity, resolution, imaging time, and radiation dose. To evaluate the resolution of our hemi-ellipsoid system, GATE Monte-Carlo simulations were performed on point-sources, rod-sources, and NCAT phantoms. The purpose of point-source simulation is to obtain operating pinhole diameter by comparing the average FWHM (Full-width-half-maximum) of flat-detector system with curved hemi-ellipsoid detector system. The operating pinhole diameter for the curved hemi-ellipsoid detector was found to be 8.68mm. System resolution is evaluated using reconstructed rod-sources equally spaced within the region of interest. The results were compared with results of GE discovery system available in the literature. The system performance was also evaluated using the mathematical anthropomorphic NCAT (NURBS-based Cardiac Torso) phantom with a full (clinical) dose acquisition (25mCi) for 2 mins and an ultra-low-dose acquisition of 3mCi for 5.44mins. On rod-sources, the average resolution after reconstruction with resolution recovery in the entire region of interest (ROI) for cardiac imaging was 4.44mm, with standard deviation 2.84mm, compared to 6.9mm reported for GE Discovery (Kennedy et al, JNC, 2014). For NCAT studies improved sensitivity allowed a full-dose (25mCi) 2 min acquisition (ELL8.68mmFD) which yielded 3.79M LV counts. This is ~3.35 times higher compared to 1.13M LV counts acquired in 2 mins for clinical full-dose for state-of-the-art DSPECT. The increased sensitivity also allowed an ultra-low dose acquisition protocol (ELL8.68mmULD). This ultra-low-dose protocol yielded ~1.23M LV-counts which was comparable to the full-dose 2min acquisition for DSPECT. The estimated NCAT average FWHM at the LV wall after 12 iterations of the OSEM reconstruction was 4.95mm and 5.66mm around the mid-short-axis slices for ELL8.68mmFD and ELL8.68mmULD respectively

Deep Q-Learning-based Distribution Network Reconfiguration for Reliability Improvement

Distribution network reconfiguration (DNR) has proved to be an economical and effective way to improve the reliability of distribution systems. As optimal network configuration depends on system operating states (e.g., loads at each node), existing analytical and population-based approaches need to repeat the entire analysis and computation to find the optimal network configuration with a change in system operating states. Contrary to this, if properly trained, deep reinforcement learning (DRL)-based DNR can determine optimal or near-optimal configuration quickly even with changes in system states. In this paper, a Deep Q Learning-based framework is proposed for the optimal DNR to improve reliability of the system. An optimization problem is formulated with an objective function that minimizes the average curtailed power. Constraints of the optimization problem are radial topology constraint and all nodes traversing constraint. The distribution network is modeled as a graph and the optimal network configuration is determined by searching for an optimal spanning tree. The optimal spanning tree is the spanning tree with the minimum value of the average curtailed power. The effectiveness of the proposed framework is demonstrated through several case studies on 33-node and 69-node distribution test systems