Learning and Control Applied to Demand Response and Electricity Distribution Networks

Balancing the supply and demand of electrical energy in real-time is a core task in power system operation. Traditionally, this balance has been achieved by controlling power plants, but increasing amounts of renewable energy generation increases the variability in generation and requires additional energy balancing capacity. An alternative to providing this additional capacity via power plants is to provide signals to loads that induce changes in their demand, which is referred to as demand response. There exists a large potential capacity for demand response using residential loads, but enabling these loads to participate in demand response requires communication and sensing capabilities. Thermostatically controlled loads (TCLs) are ubiquitous in residences and have inherent flexibility as they cycle on and off during normal operation. Coordinating on/off switching of TCL aggregations can provide energy balancing. However, TCLs are a spatially distributed resource that require sensing and communication infrastructure to enable demand response capabilities. A key to realizing cost effective residential demand response is minimizing infrastructure costs while maximizing the accuracy of the provided energy balancing, which results in increased revenue while improving reliability in the power system. The main contribution of this dissertation is to show that advanced algorithms can leverage existing infrastructure to make energy balancing with loads feasible in the near-term, which improves the reliability, economics, and environmental impact of the power grid. The dissertation first presents control algorithms, estimation algorithms, and models for residential demand response on fast timescales, i.e., on the order of seconds. Following this, the dissertation presents online learning algorithms for real-time feeder-level energy disaggregation within an electricity distribution network, which can be used to estimate the demand-responsive load in real-time. Methods for both topics are developed to operate within the capabilities of existing communication and sensing infrastructure, which reduces the implementation costs of the methods. Control and estimation algorithms are developed that address communication delays while taking into account realistic measurement availability. Results indicate that incorporating delay information into the algorithms can mitigate the effects of communication delays, allowing demand response providers to reduce infrastructure costs by using less expensive, lower quality communication networks. Additional work adapts three existing residential demand response models for a more detailed simulation environment, modifies each model to be more accurate in this environment, and benchmarks the model variations against each other. Results indicate that the model modifications produce more accurate predictions versus the unmodified models. Improving modeling accuracy can improve the reliability of the system and increase revenues for a demand response provider by improving the performance of model-based control and estimation algorithms. The energy disaggregation algorithms seek to separate the measured demand of a distribution feeder into components (e.g., the demand-responsive load and the remaining load) as feeder-level measurements become available. An online learning algorithm is adapted to perform real-time energy disaggregation using active power measurements of the total demand on the distribution feeder. Results indicate that the algorithm is able to effectively separate the air conditioning demand from the remaining demand connected to a distribution feeder. This algorithm is then extended to include reactive power, voltage, and smart meter measurements. Results indicate that the availability of additional real-time measurements leads to more accurate disaggregation of the demand components. Additional work in state estimation establishes connections between the online learning methods used and Kalman filtering algorithms.PHDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/149905/1/gsledv_1.pd

PMU-Based ROCOF Measurements: Uncertainty Limits and Metrological Significance in Power System Applications

In modern power systems, the Rate-of-Change-of-Frequency (ROCOF) may be largely employed in Wide Area Monitoring, Protection and Control (WAMPAC) applications. However, a standard approach towards ROCOF measurements is still missing. In this paper, we investigate the feasibility of Phasor Measurement Units (PMUs) deployment in ROCOF-based applications, with a specific focus on Under-Frequency Load-Shedding (UFLS). For this analysis, we select three state-of-the-art window-based synchrophasor estimation algorithms and compare different signal models, ROCOF estimation techniques and window lengths in datasets inspired by real-world acquisitions. In this sense, we are able to carry out a sensitivity analysis of the behavior of a PMU-based UFLS control scheme. Based on the proposed results, PMUs prove to be accurate ROCOF meters, as long as the harmonic and inter-harmonic distortion within the measurement pass-bandwidth is scarce. In the presence of transient events, the synchrophasor model looses its appropriateness as the signal energy spreads over the entire spectrum and cannot be approximated as a sequence of narrow-band components. Finally, we validate the actual feasibility of PMU-based UFLS in a real-time simulated scenario where we compare two different ROCOF estimation techniques with a frequency-based control scheme and we show their impact on the successful grid restoration.Comment: Manuscript IM-18-20133R. Accepted for publication on IEEE Transactions on Instrumentation and Measurement (acceptance date: 9 March 2019

Secure Distributed Dynamic State Estimation in Wide-Area Smart Grids

Smart grid is a large complex network with a myriad of vulnerabilities, usually operated in adversarial settings and regulated based on estimated system states. In this study, we propose a novel highly secure distributed dynamic state estimation mechanism for wide-area (multi-area) smart grids, composed of geographically separated subregions, each supervised by a local control center. We firstly propose a distributed state estimator assuming regular system operation, that achieves near-optimal performance based on the local Kalman filters and with the exchange of necessary information between local centers. To enhance the security, we further propose to (i) protect the network database and the network communication channels against attacks and data manipulations via a blockchain (BC)-based system design, where the BC operates on the peer-to-peer network of local centers, (ii) locally detect the measurement anomalies in real-time to eliminate their effects on the state estimation process, and (iii) detect misbehaving (hacked/faulty) local centers in real-time via a distributed trust management scheme over the network. We provide theoretical guarantees regarding the false alarm rates of the proposed detection schemes, where the false alarms can be easily controlled. Numerical studies illustrate that the proposed mechanism offers reliable state estimation under regular system operation, timely and accurate detection of anomalies, and good state recovery performance in case of anomalies

Choice of State Estimation Solution Process for Medium Voltage Distribution Systems

As distribution networks are turning into active systems, enhanced observability and continuous monitoring becomes essential for effective management and control. The state estimation (SE) tool is therefore now considered as the core component in future distribution management systems. The development of a novel distribution system SE tool is required to accommodate small to very large networks, operable with limited real time measurements and able to execute the computation of large volumes of data in a limited time frame. In this context, the paper investigates the computation time and voltage estimation qualities of three different SE optimization solution methods in order to evaluate their effectiveness as potential distribution SE candidate solutions

Every Moment Counts: Synchrophasors for Distribution Networks with Variable Resources

Chapter 34 in the textbook, "Renewable Energy Integration: Practical Management of Variability, Uncertainty and Flexibility

Adjustment of model parameters to estimate distribution transformers remaining lifespan

Currently, the electrical system in Argentina is working at its maximum capacity, decreasing the margin between the installed power and demanded consumption, and drastically reducing the service life of transformer substations due to overload (since the margin for summer peaks is small). The advent of the Smart Grids allows electricity distribution companies to apply data analysis techniques to manage resources more efficiently at different levels (avoiding damages, better contingency management, maintenance planning, etc.). The Smart Grids in Argentina progresses slowly due to the high costs involved. In this context, the estimation of the lifespan reduction of distribution transformers is a key tool to efficiently manage human and material resources, maximizing the lifetime of this equipment. Despite the current state of the smart grids, the electricity distribution companies can implement it using the available data. Thermal models provide guidelines for lifespan estimation, but the adjustment to particular conditions, brands, or material quality is done by adjusting parameters. In this work we propose a method to adjust the parameters of a thermal model using Genetic Algorithms, comparing the estimation values of top-oil temperature with measurements from 315 kVA distribution transformers, located in the province of Tucumán, Argentina. The results show that, despite limited data availability, the adjusted model is suitable to implement a transformer monitoring system.Fil: Jimenez, Victor Adrian. Universidad Tecnológica Nacional. Facultad Regional Tucumán. Centro de Investigación en Tecnologías Avanzadas de Tucumán; ArgentinaFil: Will, Adrian L. E.. Universidad Tecnológica Nacional. Facultad Regional Tucumán. Centro de Investigación en Tecnologías Avanzadas de Tucumán; ArgentinaFil: Gotay Sardiñas, Jorge. Universidad Tecnológica Nacional. Facultad Regional Tucumán. Centro de Investigación en Tecnologías Avanzadas de Tucumán; ArgentinaFil: Rodriguez, Sebastian Alberto. Universidad Tecnológica Nacional. Facultad Regional Tucumán. Centro de Investigación en Tecnologías Avanzadas de Tucumán; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán; Argentin

Estimating Dynamic Load Parameters from Ambient PMU Measurements

In this paper, a novel method to estimate dynamic load parameters via ambient PMU measurements is proposed. Unlike conventional parameter identification methods, the proposed algorithm does not require the existence of large disturbance to power systems, and is able to provide up-to-date dynamic load parameters consistently and continuously. The accuracy and robustness of the method are demonstrated through numerical simulations.Comment: The paper has been accepted by IEEE PES general meeting 201

Smart Grid Security: Threats, Challenges, and Solutions

The cyber-physical nature of the smart grid has rendered it vulnerable to a multitude of attacks that can occur at its communication, networking, and physical entry points. Such cyber-physical attacks can have detrimental effects on the operation of the grid as exemplified by the recent attack which caused a blackout of the Ukranian power grid. Thus, to properly secure the smart grid, it is of utmost importance to: a) understand its underlying vulnerabilities and associated threats, b) quantify their effects, and c) devise appropriate security solutions. In this paper, the key threats targeting the smart grid are first exposed while assessing their effects on the operation and stability of the grid. Then, the challenges involved in understanding these attacks and devising defense strategies against them are identified. Potential solution approaches that can help mitigate these threats are then discussed. Last, a number of mathematical tools that can help in analyzing and implementing security solutions are introduced. As such, this paper will provide the first comprehensive overview on smart grid security