Matching Items (25)
Description
Low frequency oscillations (LFOs) are recognized as one of the most challenging problems in electric grids as they limit power transfer capability and can result in system instability. In recent years, the deployment of phasor measurement units (PMUs) has increased the accessibility to time-synchronized wide-area measurements, which has, in turn, enabledthe effective detection and control of the oscillatory modes of the power system. This work assesses the stability improvements that can be achieved through the coordinated wide-area control of power system stabilizers (PSSs), static VAr compensators (SVCs), and supplementary damping controllers (SDCs) of high voltage DC (HVDC) lines, for damping electromechanical oscillations in a modern power system. The improved damping is achieved by designing different types of coordinated wide-area damping controllers (CWADC) that employ partial state-feedback. The first design methodology uses a linear matrix inequality (LMI)-based mixed H2/Hinfty control that is robust for multiple operating scenarios. To counteract the negative impact of communication failure or missing PMU measurements on the designed control, a scheme to identify the alternate set of feedback signals is proposed. Additionally, the impact of delays on the performance of the control design is investigated.
The second approach is motivated by the increasing popularity of artificial intelligence (AI) in enhancing the performance of interconnected power systems. Two different wide-area coordinated control schemes are developed using deep neural networks (DNNs) and deep reinforcement learning (DRL), while accounting for the uncertainties present in the power system. The DNN-CWADC learns to make control decisions using supervised learning; the training dataset consisting of polytopic controllers designed with the help of LMI-based mixed H2/Hinfty optimization. The DRL-CWADC learns to adapt to the system uncertainties based on its continuous interaction with the power system environment by employing
an advanced version of the state-of-the-art deep deterministic policy gradient (DDPG) algorithm referred to as bounded exploratory control-based DDPG (BEC-DDPG). The studies performed on a 29 machine, 127 bus equivalent model of theWestern Electricity Coordinating Council (WECC) system-embedded with different types of damping controls have demonstrated the effectiveness and robustness of the proposed CWADCs.
ContributorsGupta, Pooja (Author) / Pal, Anamitra (Thesis advisor) / Vittal, Vijay (Thesis advisor) / Zhang, Junshan (Committee member) / Hedmnan, Mojdeh (Committee member) / Wu, Meng (Committee member) / Arizona State University (Publisher)
Created2021
Description
Ensuring reliable operation of large power systems subjected to multiple outages is a challenging task because of the combinatorial nature of the problem. Traditional methods of steady-state security assessment in power systems involve contingency analysis based on AC or DC power flows. However, power flow based contingency analysis is not fast enough to evaluate all contingencies for real-time operations. Therefore, real-time contingency analysis (RTCA) only evaluates a subset of the contingencies (called the contingency list), and hence might miss critical contingencies that lead to cascading failures.This dissertation proposes a new graph-theoretic approach, called the feasibility test (FT) algorithm, for analyzing whether a contingency will create a saturated or over-loaded cut-set in a meshed power network; a cut-set denotes a set of lines which if tripped separates the network into two disjoint islands. A novel feature of the proposed approach is that it lowers the solution time significantly making the approach viable for an exhaustive real-time evaluation of the system. Detecting saturated cut-sets in the power system is important because they represent the vulnerable bottlenecks in the network. The robustness of the FT algorithm is demonstrated on a 17,000+ bus model of the Western Interconnection (WI).
Following the detection of post-contingency cut-set saturation, a two-component methodology is proposed to enhance the reliability of large power systems during a series of outages. The first component combines the proposed FT algorithm with RTCA to create an integrated corrective action (iCA), whose goal is to secure the power system against post-contingency cut-set saturation as well as critical branch overloads. The second component only employs the results of the FT to create a relaxed corrective action (rCA) that quickly secures the system against saturated cut-sets.
The first component is more comprehensive than the second, but the latter is computationally more efficient. The effectiveness of the two components is evaluated based upon the number of cascade triggering contingencies alleviated, and the computation time. Analysis of different case-studies on the IEEE 118-bus and 2000-bus synthetic Texas systems indicate that the proposed two-component methodology enhances the scope and speed of power system security assessment during multiple outages.
ContributorsSen Biswas, Reetam (Author) / Pal, Anamitra (Thesis advisor) / Vittal, Vijay (Committee member) / Undrill, John (Committee member) / Wu, Meng (Committee member) / Zhang, Yingchen (Committee member) / Arizona State University (Publisher)
Created2021
Description
Due to the new and old challenges, modern-day market management systems continue to evolve, including market reformulations, introducing new market products, and proposing new frameworks for integrating distributed energy resources (DERs) into the wholesale markets. Overall, questions is regarding how to reflect these essential changes in the market models (design, reformulation, and coordination frameworks), design market-based incentive structures to adequately compensate participants for providing ancillary services, and assess these impacts on market settlements.First, this dissertation proposes the concept of securitized-LMP to solve the issue of how market participants should be compensated for providing N-1 reliability services. Then, pricing implications and settlements of three state-of-art market models are compared. The results show that with a more accurate representation of contingencies in the market models, N-1 grid security requirements are originally captured; thereby, the value of service provided by generators is reflected in the prices to achieve grid security.
Also, new flexible ramping product (FRP) designs are proposed for different market processes to (i) schedule day-ahead (DA) FRP awards that are more adaptive concerning the real-time (RT) 15-min net load changes, and (ii) address the FRP deployability issue in fifteen-minute market (FMM). The proposed market models performance with enhanced FRP designs is compared against the DA market and FMM models with the existing FRP design through a validation methodology based on California independent system operator (ISO) RT operation. The proposed FRP designs lead to less expected final RT operating cost, higher reliability, and fewer RT price spikes.
Finally, this dissertation proposes a distribution utility and ISO coordination framework to enable ISO to manage the wholesale market while preemptively not allowing aggregators to cause distribution system (DS) violations. To this end, this coordination framework architecture utilizes the statistical information obtained using different DS conditions and data-mining algorithms to predict the aggregators qualified maximum capacity. A validation phase considering Volt-VAr support provided by distributed PV smart inverters is utilized for evaluate the proposed model performance. The proposed model produces wholesale market awards for aggregators that fall within the DS operational limits and, consequently, will not impose reliable and safety issues for the DS.
ContributorsGhaljehei, Mohammad (Author) / Khorsand, Mojdeh (Thesis advisor) / Vittal, Vijay (Committee member) / Wu, Meng (Committee member) / Weng, Yang (Committee member) / Arizona State University (Publisher)
Created2022
Description
The increase in the photovoltaic (PV) generation on distribution grids may cause reverse power flows and challenges such as service voltage violations and transformer overloading. To resolve these issues, utilities need situational awareness, e.g., PV-feeder mapping to identify the potential back-feeding feeders and meter-transformer mapping for transformer overloading. As circuit schematics are outdated, this work relies on data. In cases where the advanced metering infrastructure (AMI) data is unavailable, e.g., analog meters or bandwidth limitation, the dissertation proposes to use feeder measurements from utilities and solar panel measurements from solar companies to identify PV-feeder mapping. Several sequentially improved methods based on quantitative association rule mining (QARM) are proposed, where a lower bound for performance guarantee is also provided. However, binning data in QARM leads to information loss. So, bands are designed to replace bins for increased robustness. For cases where AMI data is available but solar PV data is unavailable, the AMI voltage data and location data are used for situational awareness, i.e., meter-transformer mapping, to resolve voltage violation and transformer overloading. A density-based clustering method is proposed that leverages AMI voltage data and geographical information to efficiently segment utility meters such that the segments comprise meters of few transformers only. Although it is helpful for utilities, it may not directly recover the meter-transformer connectivity, which requires transformer-wise segmentation. The proposed density-based method and other past methods ignore two common scenarios, e.g., having large distance between a meter and parent transformer or high similarity of a meter's consumption pattern to a non-parent transformer's meters. However, going from meter-meter can lead to the parent transformer group meters due to the usual observation that the similarity of intra-cluster meter voltages is usually stronger than the similarity of inter-cluster meter voltages. Therefore, performance guarantee is provided via spectral embedding with voltage data under reasonable assumption. Moreover, the assumption is partially relaxed using location data. It will benefit the utility in many ways, e.g., mitigating voltage violations by transformer tap settings and identifying overloaded transformers.
ContributorsSaleem, Muhammad Bilal (Author) / Weng, Yang (Thesis advisor) / Lanchier, Nicolas (Committee member) / Wu, Meng (Committee member) / Cook, Elizabeth (Committee member) / Arizona State University (Publisher)
Created2022
Description
With the increasing penetration levels of distributed energy resources along distribution feeders, the importance of load modeling has grown significantly and therefore it is important to have an accurate representation of the distribution system in the planning and operation studies. Although, currently, most of the power system studies are being done using positive sequence commercial software packages for computational convenience purposes, it comes at the cost of reduced accuracy when compared to the more accurate electromagnetic transient (EMT) simulators (but more computationally intensive). However, it is expected, that in the next several years, the use of EMT simulators for large-scale system studies would become a necessity to implement the ambitious renewable energy targets adopted by many countries across the world. Currently, the issue of developing more accurate EMT feeder and load models has yet to be addressed. Therefore, in the first phase of this work, an optimization algorithm to synthesize an EMT distribution feeder and load model has been developed by capturing the current transients when three-phase voltage measurements (obtained from a local utility) are played-in as input, from events such as sub-transmission faults, to the synthesized model. Using the developed algorithm, for the proposed feeder model, both the load composition and the load parameters have been estimated. The synthesized load model has a load composition which includes impedance loads, single-phase induction motor (SPHIM) loads and three-phase induction motor loads.
In the second phase of this work, an analytical formulation of a 24 V EMT contactor is developed to trip the air conditioner EMT SPHIM load, in the feeder and load model developed in Phase 1 of this work, under low voltage conditions. Additionally, a new methodology is developed, to estimate and incorporate the trip and reconnection settings of the proposed EMT contactor model to trip, reconnect and stall the SPHIMs in a positive sequence simulator (PSLF) for single-line to ground faults. Also, the proposed methodology has been tested on a modified three-segment three-phase feeder model using a local utility’s practical feeder topological and loading information. Finally, the developed methodology is modified to accommodate three-phase faults in the system.
ContributorsNekkalapu, Sameer (Author) / Vittal, Vijay (Thesis advisor) / Undrill, John (Committee member) / Ayyanar, Raja (Committee member) / Wu, Meng (Committee member) / Arizona State University (Publisher)
Created2022
Description
In this dissertation, a distribution system operator (DSO) framework is proposed to optimally coordinate distributed energy resources (DER) aggregators' comprehensive participation in the retail energy market as well as wholesale energy and regulation markets. Various types of DER aggregators, including energy storage aggregators (ESAGs), dispatchable distributed generation aggregators (DDGAGs), electric vehicles charging stations (EVCSs), and demand response aggregators (DRAGs), are modeled in the proposed DSO framework. An important characteristic of a DSO is being capable of handling uncertainties in the system operation. An appropriate method for a market operator to cover uncertainties is using two-stage stochastic programming. To handle comprehensive retail and wholesale markets participation of distributed energy resource (DER) aggregators under uncertainty, a two-stage stochastic programming model for the DSO is proposed. To handle unbalanced distribution grids with single-phase aggregators, A DSO framework is proposed for unbalanced distribution networks based on a linearized unbalanced power flow which coordinates with wholesale market clearing process and ensures the DSO's non-profit characteristic.
When proposing a DSO, coordination with the ISO is important. A framework is proposed to coordinate the operation of the independent system operator (ISO) and distribution system operator (DSO). The framework is compatible with current practice of the U.S. wholesale market to enable massive distributed energy resources (DERs) to participate in the wholesale market. The DSO builds a bid-in cost function to be submitted to the ISO market through parametric programming. A pricing problem for the DSO is proposed. In pricing problem, after ISO clears the wholesale market, the locational marginal price (LMP) of the ISO-DSO coupling substation is determined, the DSO utilizes this price to solve the DSO pricing problem. The DSO pricing problem determines the distribution LMP (D-LMP) in the distribution system and calculates the payment to each aggregator. An efficient algorithm is proposed to solve the ISO-DSO coordination parametric programming problem. Notably, our proposed algorithm significantly improves the computational efficiency of solving the parametric programming DSO problem which is computationally intensive.
Various case studies are performed to analyze the market outcome of the proposed DSO framework and coordination with the ISO.
ContributorsMousavi, Mohammad (Author) / Wu, Meng (Thesis advisor) / Khorsand, Mojdeh (Committee member) / Byeon, Geunyeong (Committee member) / Nguyen, Duong (Committee member) / Arizona State University (Publisher)
Created2023
Description
The integration of Distributed Energy Resources (DER), including wind energy and photovoltaic (PV) panels, into power systems, increases the potential for events that could lead to outages and cascading failures. This risk is heightened by the limited dynamic information in energy grid datasets, primarily due to sparse Phasor Measurement Units (PMUs) placement. This data quality issue underscores the need for effective methodologies to manage these challenges. One significant challenge is the data gaps in low-resolution (LR) data from RTU and smart meters, hindering robust machine learning (ML) applications. To address this, a systematic approach involves preparing data effectively and designing efficient event detection methods, utilizing both intrinsic physics and extrinsic correlations from power systems. The process begins by interpolating LR data using high-resolution (HR) data, aiming to create virtual PMUs for improved grid management. Current interpolation methods often overlook extrinsic spatial-temporal correlations and intrinsic governing equations like Ordinary Differential Equations (ODEs) or Differential Algebraic Equations (DAEs). Physics-Informed Neural Networks (PINNs) are used for this purpose, though they face challenges with limited LR samples. The solution involves exploring the embedding space governed by ODEs/DAEs, generating extrinsic correlations for initial LR data imputation, and enforcing intrinsic physical constraints for refinement. After data preparation, event data dimensions such as spatial, temporal, and measurement categories are recovered in a tensor. To prevent overfitting, common in traditional ML methods, tensor decomposition is used. This technique merges intrinsic and physical information across dimensions, yielding informative and compact feature vectors for efficient feature extraction and learning in event detection. Lastly, in grids with insufficient data, knowledge transfer from grids with similar event patterns is a viable solution. This involves optimizing projected and transferred vectors from tensor decomposition to maximize common knowledge utilization across grids. This strategy identifies common features, enhancing the robustness and efficiency of ML event detection models, even in scenarios with limited event data.
ContributorsMa, Zhihao (Author) / Weng, Yang (Thesis advisor) / Wu, Meng (Committee member) / Yu, Hongbin (Committee member) / Matavalam, Amarsagar Reddy Ramapuram (Committee member) / Arizona State University (Publisher)
Created2023
Description
Renewable energy and carbon reduction policies are creating new challenges for electricity markets. To achieve carbon-free goals, large-scale battery energy storage systems (BESSs) are necessary to ensure grid reliability and flexibility. The impact of BESSs on market and grid operation, as well as the optimal portfolio across the energy and ancillary services markets, must be analyzed to guide their operation. At the same time, the expansion of renewable and storage resources and the adoption of carbon reduction policies have introduced new complexities to the bidding behavior of market participants, which cannot be easily described by cost-based bidding objectives. In response to these challenges, this dissertation aims to achieve two research objectives: (I) enable BESS participation in energy and ancillary services markets under uncertainties, considering the battery's degradation cost; (II) identify robust bidding objectives for electricity market participants based on their historical bidding data. Three optimization frameworks are proposed in Part I to model a BESS as a price-maker in energy markets, evaluating its impact on market outcomes. The preliminary framework models automatic generation control signals, while the detailed framework proposes a participation factor for dispatching AGC signals and accounts for battery degradation costs. The stochastic framework models spinning reserve deployment with uncertainty and propose an optimization-based approximation method based on reinforcement learning. Case studies on proposed frameworks validate operational models for Battery Energy Storage Systems (BESS) and markets, showing the accuracy and efficiency of the approximation approach. Key findings include that accurate degradation cost modeling is essential, and participation in ancillary services markets is more profitable. Part II proposes a data-driven approach using Adversarial Inverse Reinforcement Learning to identify robust bidding objectives for electricity market participants. It introduces a tailored reinforcement learning model for bidding objective identification without data discretization, and a special policy structure compliant with multi-segment bidding rules. Two approaches are suggested for electricity market environment modeling in RL/IRL problems, ensuring the robustness of the identified bidding objective. Three case studies validated the accuracy and robustness of the proposed bidding objective identification method in various application scenarios.
ContributorsKhalilisenobari, Reza (Author) / Wu, Meng (Thesis advisor) / Vitall, Vijay (Committee member) / Pal, Anamitra (Committee member) / Khorsand, Mojdeh (Committee member) / Arizona State University (Publisher)
Created2023
Description
This report studies the optimal mechanisms for the vertically integrated utility to dispatch and incentivize the third-party demand response (DR) providers in its territory. A framework is proposed, with three-layer coupled Stackelberg and simultaneous games, to study the interactions and competitions among the pro t-seeking process of the utility, the third-party DR providers, and the individual end users (EUs) in the DR programs. Two coupled single-leader-multiple-followers Stackelberg games with a three-layer structure are proposed to capture the interactions among the utility (modeled in the upper layer), the third-party DR providers (modeled in the middle layer), and the EUs in each DR program (modeled in the lower layer). The competitions among the EUs in each DR program is captured through a non-cooperative simultaneous game. An inconvenience cost function is proposed to model the DR provision willingness and capacity of different EUs. The Stackelberg game between the middle-layer DR provider and the lower-layer EUs is solved by converting the original bi-level programming to a single level programming. This converted single level programming is embedded in an iterative algorithm toward solving the entire coupled games framework. Case studies are performed on IEEE 34-bus and IEEE69-bus test systems to illustrate the application of the proposed framework.
ContributorsSajjadi, Sayyid Mohssen (Author) / Wu, Meng (Thesis advisor) / Hedman, Mojdeh (Committee member) / Pal, Anamitra (Committee member) / Arizona State University (Publisher)
Created2023
Description
Large number of renewable energy based distributed energy resources(DERs) are integrated into the conventional power grid using power electronic interfaces. This causes increased need for efficient power conversion, advanced control, and DER situational awareness. In case of photovoltaic(PV) grid integration, power is processed in two stages, namely DC-DC and DC-AC. In this work, two novel soft-switching schemes for quadratic boost DC-DC converters are proposed for PV microinverter application. Both the schemes allow the converter to operate at higher switching frequency, reducing the converter size while still maintaining high power conversion efficiency. Further, to analyze the impact of high penetration DERs on the power system a real-time simulation platform has been developed in this work. A real, large distribution feeder with more than 8000 buses is considered for investigation. The practical challenges in the implementation of a real-time simulation (such as number of buses, simulation time step, and computational burden) and the corresponding solutions are discussed. The feeder under study has a large number of DERs leading to more than 200% instantaneous PV penetration. Opal-RT ePHASORSIM model of the distribution feeder and different types of DER models are discussed in detailed in this work. A novel DER-Edge-Cloud based three-level architecture is proposed for achieving solar situational awareness for the system operators and for real-time control of DERs. This is accomplished using a network of customized edge-intelligent-devices(EIDs) and end-to-end solar energy optimization platform(eSEOP). The proposed architecture attains superior data resolution, data transfer rate and low latency for the end-to-end communication. An advanced PV string inverter with control and communication capabilities exceeding those of state-of-the-art, commercial inverters has been developed to demonstrate the proposed real-time control. A power-hardware-in-loop(PHIL) and EID-in-loop(EIL) testbeds are developed to verify the impact of large number of controllable DERs on the distribution system under different operational modes such as volt-VAr, constant reactive power and constant power factor. Edge level data analytics and intelligent controls such as autonomous reactive power allocation strategy are implemented using EIL testbed for real-time monitoring and control. Finally, virtual oscillator control(VOC) for grid forming inverters and its operation under different X/R conditions are explored.
ContributorsKorada, Nikhil (Author) / Ayyanar, Raja (Thesis advisor) / Lei, Qin (Committee member) / Wu, Meng (Committee member) / Srinivasan, Devarajan (Committee member) / Arizona State University (Publisher)
Created2022