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- All Subjects: energy
- Member of: ASU Electronic Theses and Dissertations
- Status: Published
Description
Transmission expansion planning (TEP) is a complex decision making process that requires comprehensive analysis to determine the time, location, and number of electric power transmission facilities that are needed in the future power grid. This dissertation investigates the topic of solving TEP problems for large power systems. The dissertation can be divided into two parts. The first part of this dissertation focuses on developing a more accurate network model for TEP study. First, a mixed-integer linear programming (MILP) based TEP model is proposed for solving multi-stage TEP problems. Compared with previous work, the proposed approach reduces the number of variables and constraints needed and improves the computational efficiency significantly. Second, the AC power flow model is applied to TEP models. Relaxations and reformulations are proposed to make the AC model based TEP problem solvable. Third, a convexified AC network model is proposed for TEP studies with reactive power and off-nominal bus voltage magnitudes included in the model. A MILP-based loss model and its relaxations are also investigated. The second part of this dissertation investigates the uncertainty modeling issues in the TEP problem. A two-stage stochastic TEP model is proposed and decomposition algorithms based on the L-shaped method and progressive hedging (PH) are developed to solve the stochastic model. Results indicate that the stochastic TEP model can give a more accurate estimation of the annual operating cost as compared to the deterministic TEP model which focuses only on the peak load.
ContributorsZhang, Hui (Author) / Vittal, Vijay (Thesis advisor) / Heydt, Gerald T (Thesis advisor) / Mittelmann, Hans D (Committee member) / Hedman, Kory W (Committee member) / Arizona State University (Publisher)
Created2013
Description
The smart grid initiative is the impetus behind changes that are expected to culminate into an enhanced distribution system with the communication and control infrastructure to support advanced distribution system applications and resources such as distributed generation, energy storage systems, and price responsive loads. This research proposes a distribution-class analog of the transmission LMP (DLMP) as an enabler of the advanced applications of the enhanced distribution system. The DLMP is envisioned as a control signal that can incentivize distribution system resources to behave optimally in a manner that benefits economic efficiency and system reliability and that can optimally couple the transmission and the distribution systems. The DLMP is calculated from a two-stage optimization problem; a transmission system OPF and a distribution system OPF. An iterative framework that ensures accurate representation of the distribution system's price sensitive resources for the transmission system problem and vice versa is developed and its convergence problem is discussed. As part of the DLMP calculation framework, a DCOPF formulation that endogenously captures the effect of real power losses is discussed. The formulation uses piecewise linear functions to approximate losses. This thesis explores, with theoretical proofs, the breakdown of the loss approximation technique when non-positive DLMPs/LMPs occur and discusses a mixed integer linear programming formulation that corrects the breakdown. The DLMP is numerically illustrated in traditional and enhanced distribution systems and its superiority to contemporary pricing mechanisms is demonstrated using price responsive loads. Results show that the impact of the inaccuracy of contemporary pricing schemes becomes significant as flexible resources increase. At high elasticity, aggregate load consumption deviated from the optimal consumption by up to about 45 percent when using a flat or time-of-use rate. Individual load consumption deviated by up to 25 percent when using a real-time price. The superiority of the DLMP is more pronounced when important distribution network conditions are not reflected by contemporary prices. The individual load consumption incentivized by the real-time price deviated by up to 90 percent from the optimal consumption in a congested distribution network. While the DLMP internalizes congestion management, the consumption incentivized by the real-time price caused overloads.
ContributorsAkinbode, Oluwaseyi Wemimo (Author) / Hedman, Kory W (Thesis advisor) / Heydt, Gerald T (Committee member) / Zhang, Muhong (Committee member) / Arizona State University (Publisher)
Created2013
Description
Efficiency of components is an ever increasing area of importance to portable applications, where a finite battery means finite operating time. Higher efficiency devices need to be designed that don't compromise on the performance that the consumer has come to expect. Class D amplifiers deliver on the goal of increased efficiency, but at the cost of distortion. Class AB amplifiers have low efficiency, but high linearity. By modulating the supply voltage of a Class AB amplifier to make a Class H amplifier, the efficiency can increase while still maintaining the Class AB level of linearity. A 92dB Power Supply Rejection Ratio (PSRR) Class AB amplifier and a Class H amplifier were designed in a 0.24um process for portable audio applications. Using a multiphase buck converter increased the efficiency of the Class H amplifier while still maintaining a fast response time to respond to audio frequencies. The Class H amplifier had an efficiency above the Class AB amplifier by 5-7% from 5-30mW of output power without affecting the total harmonic distortion (THD) at the design specifications. The Class H amplifier design met all design specifications and showed performance comparable to the designed Class AB amplifier across 1kHz-20kHz and 0.01mW-30mW. The Class H design was able to output 30mW into 16Ohms without any increase in THD. This design shows that Class H amplifiers merit more research into their potential for increasing efficiency of audio amplifiers and that even simple designs can give significant increases in efficiency without compromising linearity.
ContributorsPeterson, Cory (Author) / Bakkaloglu, Bertan (Thesis advisor) / Barnaby, Hugh (Committee member) / Kiaei, Sayfe (Committee member) / Arizona State University (Publisher)
Created2013
Description
Redundant Binary (RBR) number representations have been extensively used in the past for high-throughput Digital Signal Processing (DSP) systems. Data-path components based on this number system have smaller critical path delay but larger area compared to conventional two's complement systems. This work explores the use of RBR number representation for implementing high-throughput DSP systems that are also energy-efficient. Data-path components such as adders and multipliers are evaluated with respect to critical path delay, energy and Energy-Delay Product (EDP). A new design for a RBR adder with very good EDP performance has been proposed. The corresponding RBR parallel adder has a much lower critical path delay and EDP compared to two's complement carry select and carry look-ahead adder implementations. Next, several RBR multiplier architectures are investigated and their performance compared to two's complement systems. These include two new multiplier architectures: a purely RBR multiplier where both the operands are in RBR form, and a hybrid multiplier where the multiplicand is in RBR form and the other operand is represented in conventional two's complement form. Both the RBR and hybrid designs are demonstrated to have better EDP performance compared to conventional two's complement multipliers. The hybrid multiplier is also shown to have a superior EDP performance compared to the RBR multiplier, with much lower implementation area. Analysis on the effect of bit-precision is also performed, and it is shown that the performance gain of RBR systems improves for higher bit precision. Next, in order to demonstrate the efficacy of the RBR representation at the system-level, the performance of RBR and hybrid implementations of some common DSP kernels such as Discrete Cosine Transform, edge detection using Sobel operator, complex multiplication, Lifting-based Discrete Wavelet Transform (9, 7) filter, and FIR filter, is compared with two's complement systems. It is shown that for relatively large computation modules, the RBR to two's complement conversion overhead gets amortized. In case of systems with high complexity, for iso-throughput, both the hybrid and RBR implementations are demonstrated to be superior with lower average energy consumption. For low complexity systems, the conversion overhead is significant, and overpowers the EDP performance gain obtained from the RBR computation operation.
ContributorsMahadevan, Rupa (Author) / Chakrabarti, Chaitali (Thesis advisor) / Kiaei, Sayfe (Committee member) / Cao, Yu (Committee member) / Arizona State University (Publisher)
Created2011
Description
Class D Amplifiers are widely used in portable systems such as mobile phones to achieve high efficiency. The demands of portable electronics for low power consumption to extend battery life and reduce heat dissipation mandate efficient, high-performance audio amplifiers. The high efficiency of Class D amplifiers (CDAs) makes them particularly attractive for portable applications. The Digital class D amplifier is an interesting solution to increase the efficiency of embedded systems. However, this solution is not good enough in terms of PWM stage linearity and power supply rejection. An efficient control is needed to correct the error sources in order to get a high fidelity sound quality in the whole audio range of frequencies. A fundamental analysis on various error sources due to non idealities in the power stage have been discussed here with key focus on Power supply perturbations driving the Power stage of a Class D Audio Amplifier. Two types of closed loop Digital Class D architecture for PSRR improvement have been proposed and modeled. Double sided uniform sampling modulation has been used. One of the architecture uses feedback around the power stage and the second architecture uses feedback into digital domain. Simulation & experimental results confirm that the closed loop PSRR & PS-IMD improve by around 30-40 dB and 25 dB respectively.
ContributorsChakraborty, Bijeta (Author) / Bakkaloglu, Bertan (Thesis advisor) / Garrity, Douglas (Committee member) / Ozev, Sule (Committee member) / Arizona State University (Publisher)
Created2012
Description
The uncertainty and variability associated with stochastic resources, such as wind and solar, coupled with the stringent reliability requirements and constantly changing system operating conditions (e.g., generator and transmission outages) introduce new challenges to power systems. Contemporary approaches to model reserve requirements within the conventional security-constrained unit commitment (SCUC) models may not be satisfactory with increasing penetration levels of stochastic resources; such conventional models pro-cure reserves in accordance with deterministic criteria whose deliverability, in the event of an uncertain realization, is not guaranteed. Smart, well-designed reserve policies are needed to assist system operators in maintaining reliability at least cost.
Contemporary market models do not satisfy the minimum stipulated N-1 mandate for generator contingencies adequately. This research enhances the traditional market practices to handle generator contingencies more appropriately. In addition, this research employs stochastic optimization that leverages statistical information of an ensemble of uncertain scenarios and data analytics-based algorithms to design and develop cohesive reserve policies. The proposed approaches modify the classical SCUC problem to include reserve policies that aim to preemptively anticipate post-contingency congestion patterns and account for resource uncertainty, simultaneously. The hypothesis is to integrate data-mining, reserve requirement determination, and stochastic optimization in a holistic manner without compromising on efficiency, performance, and scalability. The enhanced reserve procurement policies use contingency-based response sets and post-contingency transmission constraints to appropriately predict the influence of recourse actions, i.e., nodal reserve deployment, on critical transmission elements.
This research improves the conventional deterministic models, including reserve scheduling decisions, and facilitates the transition to stochastic models by addressing the reserve allocation issue. The performance of the enhanced SCUC model is compared against con-temporary deterministic models and a stochastic unit commitment model. Numerical results are based on the IEEE 118-bus and the 2383-bus Polish test systems. Test results illustrate that the proposed reserve models consistently outperform the benchmark reserve policies by improving the market efficiency and enhancing the reliability of the market solution at reduced costs while maintaining scalability and market transparency. The proposed approaches require fewer ISO discretionary adjustments and can be employed by present-day solvers with minimal disruption to existing market procedures.
Contemporary market models do not satisfy the minimum stipulated N-1 mandate for generator contingencies adequately. This research enhances the traditional market practices to handle generator contingencies more appropriately. In addition, this research employs stochastic optimization that leverages statistical information of an ensemble of uncertain scenarios and data analytics-based algorithms to design and develop cohesive reserve policies. The proposed approaches modify the classical SCUC problem to include reserve policies that aim to preemptively anticipate post-contingency congestion patterns and account for resource uncertainty, simultaneously. The hypothesis is to integrate data-mining, reserve requirement determination, and stochastic optimization in a holistic manner without compromising on efficiency, performance, and scalability. The enhanced reserve procurement policies use contingency-based response sets and post-contingency transmission constraints to appropriately predict the influence of recourse actions, i.e., nodal reserve deployment, on critical transmission elements.
This research improves the conventional deterministic models, including reserve scheduling decisions, and facilitates the transition to stochastic models by addressing the reserve allocation issue. The performance of the enhanced SCUC model is compared against con-temporary deterministic models and a stochastic unit commitment model. Numerical results are based on the IEEE 118-bus and the 2383-bus Polish test systems. Test results illustrate that the proposed reserve models consistently outperform the benchmark reserve policies by improving the market efficiency and enhancing the reliability of the market solution at reduced costs while maintaining scalability and market transparency. The proposed approaches require fewer ISO discretionary adjustments and can be employed by present-day solvers with minimal disruption to existing market procedures.
ContributorsSinghal, Nikita Ghanshyam (Author) / Hedman, Kory W (Thesis advisor) / Vittal, Vijay (Committee member) / Sankar, Lalitha (Committee member) / Pal, Anamitra (Committee member) / Arizona State University (Publisher)
Created2018
Description
Synthetic power system test cases offer a wealth of new data for research and development purposes, as well as an avenue through which new kinds of analyses and questions can be examined. This work provides both a methodology for creating and validating synthetic test cases, as well as a few use-cases for how access to synthetic data enables otherwise impossible analysis.
First, the question of how synthetic cases may be generated in an automatic manner, and how synthetic samples should be validated to assess whether they are sufficiently ``real'' is considered. Transmission and distribution levels are treated separately, due to the different nature of the two systems. Distribution systems are constructed by sampling distributions observed in a dataset from the Netherlands. For transmission systems, only first-order statistics, such as generator limits or line ratings are sampled statistically. The task of constructing an optimal power flow case from the sample sets is left to an optimization problem built on top of the optimal power flow formulation.
Secondly, attention is turned to some examples where synthetic models are used to inform analysis and modeling tasks. Co-simulation of transmission and multiple distribution systems is considered, where distribution feeders are allowed to couple transmission substations. Next, a distribution power flow method is parametrized to better account for losses. Numerical values for the parametrization can be statistically supported thanks to the ability to generate thousands of feeders on command.
First, the question of how synthetic cases may be generated in an automatic manner, and how synthetic samples should be validated to assess whether they are sufficiently ``real'' is considered. Transmission and distribution levels are treated separately, due to the different nature of the two systems. Distribution systems are constructed by sampling distributions observed in a dataset from the Netherlands. For transmission systems, only first-order statistics, such as generator limits or line ratings are sampled statistically. The task of constructing an optimal power flow case from the sample sets is left to an optimization problem built on top of the optimal power flow formulation.
Secondly, attention is turned to some examples where synthetic models are used to inform analysis and modeling tasks. Co-simulation of transmission and multiple distribution systems is considered, where distribution feeders are allowed to couple transmission substations. Next, a distribution power flow method is parametrized to better account for losses. Numerical values for the parametrization can be statistically supported thanks to the ability to generate thousands of feeders on command.
ContributorsSchweitzer, Eran (Author) / Scaglione, Anna (Thesis advisor) / Hedman, Kory W (Committee member) / Overbye, Thomas J (Committee member) / Monti, Antonello (Committee member) / Sankar, Lalitha (Committee member) / Arizona State University (Publisher)
Created2019
Description
With growing concern regarding environmental issues and the need for a more sustainable grid, power systems have seen a fast expansion of renewable resources in the last decade. The uncertainty and variability of renewable resources has posed new challenges on system operators. Due to its energy-shifting and fast-ramping capabilities, energy storage (ES) has been considered as an attractive solution to alleviate the increased renewable uncertainty and variability.
In this dissertation, stochastic optimization is utilized to evaluate the benefit of bulk energy storage to facilitate the integration of high levels of renewable resources in transmission systems. A cost-benefit analysis is performed to study the cost-effectiveness of energy storage. A two-step approach is developed to analyze the effectiveness of using energy storage to provide ancillary services. Results show that as renewable penetrations increase, energy storage can effectively compensate for the variability and uncertainty in renewable energy and has increasing benefits to the system.
With increased renewable penetrations, enhanced dispatch models are needed to efficiently operate energy storage. As existing approaches do not fully utilize the flexibility of energy storage, two approaches are developed in this dissertation to improve the operational strategy of energy storage. The first approach is developed using stochastic programming techniques. A stochastic unit commitment (UC) is solved to obtain schedules for energy storage with different renewable scenarios. Operating policies are then constructed using the solutions from the stochastic UC to efficiently operate energy storage across multiple time periods. The second approach is a policy function approach. By incorporating an offline analysis stage prior to the actual operating stage, the patterns between the system operating conditions and the optimal actions for energy storage are identified using a data mining model. The obtained data mining model is then used in real-time to provide enhancement to a deterministic economic dispatch model and improve the utilization of energy storage. Results show that the policy function approach outperforms a traditional approach where a schedule determined and fixed at a prior look-ahead stage is used. The policy function approach is also shown to have minimal added computational difficulty to the real-time market.
In this dissertation, stochastic optimization is utilized to evaluate the benefit of bulk energy storage to facilitate the integration of high levels of renewable resources in transmission systems. A cost-benefit analysis is performed to study the cost-effectiveness of energy storage. A two-step approach is developed to analyze the effectiveness of using energy storage to provide ancillary services. Results show that as renewable penetrations increase, energy storage can effectively compensate for the variability and uncertainty in renewable energy and has increasing benefits to the system.
With increased renewable penetrations, enhanced dispatch models are needed to efficiently operate energy storage. As existing approaches do not fully utilize the flexibility of energy storage, two approaches are developed in this dissertation to improve the operational strategy of energy storage. The first approach is developed using stochastic programming techniques. A stochastic unit commitment (UC) is solved to obtain schedules for energy storage with different renewable scenarios. Operating policies are then constructed using the solutions from the stochastic UC to efficiently operate energy storage across multiple time periods. The second approach is a policy function approach. By incorporating an offline analysis stage prior to the actual operating stage, the patterns between the system operating conditions and the optimal actions for energy storage are identified using a data mining model. The obtained data mining model is then used in real-time to provide enhancement to a deterministic economic dispatch model and improve the utilization of energy storage. Results show that the policy function approach outperforms a traditional approach where a schedule determined and fixed at a prior look-ahead stage is used. The policy function approach is also shown to have minimal added computational difficulty to the real-time market.
ContributorsLi, Nan (Author) / Hedman, Kory W (Thesis advisor) / Tylavksy, Daniel J (Committee member) / Heydt, Gerald T (Committee member) / Sankar, Lalitha (Committee member) / Arizona State University (Publisher)
Created2016
Description
A photovoltaic (PV) module is a series and parallel connection of multiple PV cells; defects in any cell can cause module power to drop. Similarly, a photovoltaic system is a series and parallel connection of multiple modules, and any low-performing module in the PV system can decrease the system output power. Defects in a solar cell include, but not limited to, the presence of cracks, potential induced degradation (PID), delamination, corrosion, and solder bond degradation. State-of-the-art characterization techniques to identify the defective cells in a module and defective module in a string are i) Current-voltage (IV) curve tracing, ii) Electroluminescence (EL) imaging, and iii) Infrared (IR) imaging. Shortcomings of these techniques include i) unsafe connection and disconnection need to be made with high voltage electrical cables, and ii) labor and time intensive disconnection of the photovoltaic strings from the system.This work presents a non-contact characterization technique to address the above two shortcomings. This technique uses a non-contact electrostatic voltmeter (ESV) along with a probe sensor to measure the surface potential of individual solar cells in a commercial module and the modules in a string in both off-grid and grid-connected systems. Unlike the EL approach, the ESV setup directly measures the surface potential by sensing the electric field lines that are present on the surface of the solar cell.
The off-grid testing of ESV on individual cells and multicells in crystalline silicon (c-Si) modules and on individual cells in cadmium telluride (CdTe) modules and individual modules in a CdTe string showed less than 2% difference in open circuit voltage compared to the voltmeter values. In addition, surface potential mapping of the defective cracked cells in a multicell module using ESV identified the dark, grey, and bright areas of EL images precisely at the exact locations shown by the EL characterization.
The on-grid testing of ESV measured the individual module voltages at maximum power point (Vmpp) and quantitatively identified the exact PID-affected module in the entire system. In addition, the poor-performing non-PID modules of a grid-connected PV system were also identified using the ESV technique.
ContributorsRaza, Hamza Ahmad (Author) / Tamizhmani, Govindasamy (Thesis advisor) / Kiaei, Sayfe (Committee member) / Bakkaloglu, Bertan (Committee member) / Hacke, Peter (Committee member) / Arizona State University (Publisher)
Created2023
Description
This work presents research on practices in the day-ahead electric energy market, including replication practices and reliability coordinators used by some market operators to demonstrate the impact these practices have on market outcomes. The practice of constraint relaxations similar to those an Independent System Operator (ISO) might perform in day-ahead market models is implemented. The benefits of these practices are well understood by the industry; however, the implications these practices have on market outcomes and system security have not been thoroughly investigated. By solving a day-ahead market model with and without select constraint relaxations and comparing the resulting market outcomes and possible effects on system security, the effect of these constraint relaxation practices is demonstrated.
Proposed market solutions are often infeasible because constraint relaxation practices and approximations that are incorporated into market models. Therefore, the dispatch solution must be corrected to ensure its feasibility. The practice of correcting the proposed dispatch solution after the market is solved is known as out-of-market corrections (OMCs), defined as any action an operator takes that modifies a proposed day-ahead dispatch solution to ensure operating and reliability requirements. The way in which OMCs affect market outcomes is illustrated through the use of different corrective procedures. The objective of the work presented is to demonstrate the implications of these industry practices and assess the impact these practices have on market outcomes.
Proposed market solutions are often infeasible because constraint relaxation practices and approximations that are incorporated into market models. Therefore, the dispatch solution must be corrected to ensure its feasibility. The practice of correcting the proposed dispatch solution after the market is solved is known as out-of-market corrections (OMCs), defined as any action an operator takes that modifies a proposed day-ahead dispatch solution to ensure operating and reliability requirements. The way in which OMCs affect market outcomes is illustrated through the use of different corrective procedures. The objective of the work presented is to demonstrate the implications of these industry practices and assess the impact these practices have on market outcomes.
ContributorsAl-Abdullah, Yousef Mohammad (Author) / Hedman, Kory W (Thesis advisor) / Vittal, Vijay (Thesis advisor) / Heydt, Gerald T (Committee member) / Sankar, Lalitha (Committee member) / Arizona State University (Publisher)
Created2016