Matching Items (9)
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- All Subjects: Electric power systems
- All Subjects: Optimization
- Creators: Hedman, Kory W
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
Due to great challenges from aggressive environmental regulations, increased demand due to new technologies and the integration of renewable energy sources, the energy industry may radically change the way the power system is operated and designed. With the motivation of studying and planning the future power system under these new challenges, the development of the new tools is required. A network equivalent that can be used in such planning tools needs to be generated based on an accurate power flow model and an equivalencing procedure that preserves the key characteristics of the original system. Considering the pervasive use of the dc power flow models, their accuracy is of great concern. The industry seems to be sanguine about the performance of dc power flow models, but recent research has shown that the performance of different formulations is highly variable. In this thesis, several dc power-flow models are analyzed theoretically and evaluated numerically in IEEE 118-bus system and Eastern Interconnection 62,000-bus system. As shown in the numerical example, the alpha-matching dc power flow model performs best in matching the original ac power flow solution. Also, the possibility of applying these dc models in the various applications has been explored and demonstrated. Furthermore, a novel hot-start optimal dc power-flow model based on ac power transfer distribution factors (PTDFs) is proposed, implemented and tested. This optimal-reactance-only dc model not only matches the original ac PF solution well, but also preserves the congestion pattern obtain from the OPF results of the original ac model. Three improved strategies were proposed for applying the bus-aggregation technique to the large-scale systems, like EI and ERCOT, to improve the execution time, and memory requirements when building a reduced equivalent model. Speed improvements of up to a factor of 200 were observed.
ContributorsQi, Yingying (Author) / Tylavsky, Daniel J (Thesis advisor) / Hedman, Kory W (Committee member) / Sankar, Lalitha (Committee member) / Arizona State University (Publisher)
Created2013
Description
As renewable energy becomes more prevalent in transmission and distribution systems, it is vital to understand the uncertainty and variability that accompany these resources. Microgrids have the potential to mitigate the effects of resource uncertainty. With the ability to exist in either an islanded mode or maintain connections with the main-grid, a microgrid can increase reliability, defer T&D; infrastructure and effectively utilize demand response. This study presents a co-optimization framework for a microgrid with solar photovoltaic generation, emergency generation, and transmission switching. Today unit commitment models ensure reliability with deterministic criteria, which are either insufficient to ensure reliability or can degrade economic efficiency for a microgrid that uses a large penetration of variable renewable resources. A stochastic mixed integer linear program for day-ahead unit commitment is proposed to account for uncertainty inherent in PV generation. The model incorporates the ability to trade energy and ancillary services with the main-grid, including the designation of firm and non-firm imports, which captures the ability to allow for reserve sharing between the two systems. In order to manage the computational complexities, a Benders' decomposition approach is utilized. The commitment schedule was validated with solar scenario analysis, i.e., Monte-Carlo simulations are conducted to test the proposed dispatch solution. For this test case, there were few deviations to power imports, 0.007% of solar was curtailed, no load shedding occurred in the main-grid, and 1.70% load shedding occurred in the microgrid.
ContributorsHytowitz, Robin Broder (Author) / Hedman, Kory W (Thesis advisor) / Heydt, Gerald T (Committee member) / Ayyanar, Raja (Committee member) / Arizona State University (Publisher)
Created2013
Description
Today, the electric power system faces new challenges from rapid developing technology and the growing concern about environmental problems. The future of the power system under these new challenges needs to be planned and studied. However, due to the high degree of computational complexity of the optimization problem, conducting a system planning study which takes into account the market structure and environmental constraints on a large-scale power system is computationally taxing. To improve the execution time of large system simulations, such as the system planning study, two possible strategies are proposed in this thesis. The first one is to implement a relative new factorization method, known as the multifrontal method, to speed up the solution of the sparse linear matrix equations within the large system simulations. The performance of the multifrontal method implemented by UMFAPACK is compared with traditional LU factorization on a wide range of power-system matrices. The results show that the multifrontal method is superior to traditional LU factorization on relatively denser matrices found in other specialty areas, but has poor performance on the more sparse matrices that occur in power-system applications. This result suggests that multifrontal methods may not be an effective way to improve execution time for large system simulation and power system engineers should evaluate the performance of the multifrontal method before applying it to their applications. The second strategy is to develop a small dc equivalent of the large-scale network with satisfactory accuracy for the large-scale system simulations. In this thesis, a modified Ward equivalent is generated for a large-scale power system, such as the full Electric Reliability Council of Texas (ERCOT) system. In this equivalent, all the generators in the full model are retained integrally. The accuracy of the modified Ward equivalent is validated and the equivalent is used to conduct the optimal generation investment planning study. By using the dc equivalent, the execution time for optimal generation investment planning is greatly reduced. Different scenarios are modeled to study the impact of fuel prices, environmental constraints and incentives for renewable energy on future investment and retirement in generation.
ContributorsLi, Nan (Author) / Tylavsky, Daniel J (Thesis advisor) / Vittal, Vijay (Committee member) / Hedman, Kory W (Committee member) / Arizona State University (Publisher)
Created2012
Description
As global energy demand has dramatically increased and traditional fossil fuels will be depleted in the foreseeable future, clean and unlimited renewable energies are recognized as the future global energy challenge solution. Today, the power grid in U.S. is building more and more renewable energies like wind and solar, while the electric power system faces new challenges from rapid growing percentage of wind and solar. Unlike combustion generators, intermittency and uncertainty are the inherent features of wind and solar. These features bring a big challenge to the stability of modern electric power grid, especially for a small scale power grid with wind and solar. In order to deal with the intermittency and uncertainty of wind and solar, energy storage systems are considered as one solution to mitigate the fluctuation of wind and solar by smoothing their power outputs. For many different types of energy storage systems, this thesis studied the operation of battery energy storage systems (BESS) in power systems and analyzed the benefits of the BESS. Unlike many researchers assuming fixed utilization patterns for BESS and calculating the benefits, this thesis found the BESS utilization patterns and benefits through an investment planning model. Furthermore, a cost is given for utilizing BESS and to find the best way of operating BESS rather than set an upper bound and a lower bound for BESS energy levels. Two planning models are proposed in this thesis and preliminary conclusions are derived from simulation results. This work is organized as below: chapter 1 briefly introduces the background of this research; chapter 2 gives an overview of previous related work in this area; the main work of this thesis is put in chapter 3 and chapter 4 contains the generic BESS model and the investment planning model; the following chapter 5 includes the simulation and results analysis of this research and chapter 6 provides the conclusions from chapter 5.
ContributorsDai, Daihong (Author) / Hedman, Kory W (Thesis advisor) / Zhang, Muhong (Committee member) / Ayyanar, Raja (Committee member) / Arizona State University (Publisher)
Created2014
Description
This thesis addresses the issue of making an economic case for bulk energy storage in the Arizona bulk power system. Pumped hydro energy storage (PHES) is used in this study. Bulk energy storage has often been suggested for large scale electric power systems in order to levelize load (store energy when it is inexpensive [energy demand is low] and discharge energy when it is expensive [energy demand is high]). It also has the potential to provide opportunities to avoid transmission and generation expansion, and provide for generation reserve margins. As the level of renewable energy resources increases, the uncertainty and variability of wind and solar resources may be improved by bulk energy storage technologies.
For this study, the MATLab software platform is used, a mathematical based modeling language, optimization solvers (specifically Gurobi), and a power flow solver (PowerWorld) are used to simulate an economic dispatch problem that includes energy storage and transmission losses. A program is created which utilizes quadratic programming to analyze various cases using a 2010 summer peak load from the Arizona portion of the Western Electricity Coordinating Council (WECC) system. Actual data from industry are used in this test bed. In this thesis, the full capabilities of Gurobi are not utilized (e.g., integer variables, binary variables). However, the formulation shown here does create a platform such that future, more sophisticated modeling may readily be incorporated.
The developed software is used to assess the Arizona test bed with a low level of energy storage to study how the storage power limit effects several optimization outputs such as the system wide operating costs. Large levels of energy storage are then added to see how high level energy storage affects peak shaving, load factor, and other system applications. Finally, various constraint relaxations are made to analyze why the applications tested eventually approach a constant value. This research illustrates the use of energy storage which helps minimize the system wide generator operating cost by "shaving" energy off of the peak demand.
The thesis builds on the work of another recent researcher with the objectives of strengthening the assumptions used, checking the solutions obtained, utilizing higher level simulation languages to affirm results, and expanding the results and conclusions.
One important point not fully discussed in the present thesis is the impact of efficiency in the pumped hydro cycle. The efficiency of the cycle for modern units is estimated at higher than 90%. Inclusion of pumped hydro losses is relegated to future work.
For this study, the MATLab software platform is used, a mathematical based modeling language, optimization solvers (specifically Gurobi), and a power flow solver (PowerWorld) are used to simulate an economic dispatch problem that includes energy storage and transmission losses. A program is created which utilizes quadratic programming to analyze various cases using a 2010 summer peak load from the Arizona portion of the Western Electricity Coordinating Council (WECC) system. Actual data from industry are used in this test bed. In this thesis, the full capabilities of Gurobi are not utilized (e.g., integer variables, binary variables). However, the formulation shown here does create a platform such that future, more sophisticated modeling may readily be incorporated.
The developed software is used to assess the Arizona test bed with a low level of energy storage to study how the storage power limit effects several optimization outputs such as the system wide operating costs. Large levels of energy storage are then added to see how high level energy storage affects peak shaving, load factor, and other system applications. Finally, various constraint relaxations are made to analyze why the applications tested eventually approach a constant value. This research illustrates the use of energy storage which helps minimize the system wide generator operating cost by "shaving" energy off of the peak demand.
The thesis builds on the work of another recent researcher with the objectives of strengthening the assumptions used, checking the solutions obtained, utilizing higher level simulation languages to affirm results, and expanding the results and conclusions.
One important point not fully discussed in the present thesis is the impact of efficiency in the pumped hydro cycle. The efficiency of the cycle for modern units is estimated at higher than 90%. Inclusion of pumped hydro losses is relegated to future work.
ContributorsDixon, William Jesse J (Author) / Heydt, Gerald T (Thesis advisor) / Hedman, Kory W (Committee member) / Karady, George G. (Committee member) / Arizona State University (Publisher)
Created2014
Description
This research develops heuristics for scheduling electric power production amid uncertainty. Reliability is becoming more difficult to manage due to growing uncertainty from renewable resources. This challenge is compounded by the risk of resource outages, which can occur any time and without warning. Stochastic optimization is a promising tool but remains computationally intractable for large systems. The models used in industry instead schedule for the forecast and withhold generation reserve for scenario response, but they are blind to how this reserve may be constrained by network congestion. This dissertation investigates more effective heuristics to improve economics and reliability in power systems where congestion is a concern.
Two general approaches are developed. Both approximate the effects of recourse decisions without actually solving a stochastic model. The first approach procures more reserve whenever approximate recourse policies stress the transmission network. The second approach procures reserve at prime locations by generalizing the existing practice of reserve disqualification. The latter approach is applied for feasibility and is later extended to limit scenario costs. Testing demonstrates expected cost improvements around 0.5%-1.0% for the IEEE 73-bus test case, which can translate to millions of dollars per year even for modest systems. The heuristics developed in this dissertation perform somewhere between established deterministic and stochastic models: providing an economic benefit over current practices without substantially increasing computational times.
Two general approaches are developed. Both approximate the effects of recourse decisions without actually solving a stochastic model. The first approach procures more reserve whenever approximate recourse policies stress the transmission network. The second approach procures reserve at prime locations by generalizing the existing practice of reserve disqualification. The latter approach is applied for feasibility and is later extended to limit scenario costs. Testing demonstrates expected cost improvements around 0.5%-1.0% for the IEEE 73-bus test case, which can translate to millions of dollars per year even for modest systems. The heuristics developed in this dissertation perform somewhere between established deterministic and stochastic models: providing an economic benefit over current practices without substantially increasing computational times.
ContributorsLyon, Joshua Daniel (Author) / Zhang, Muhong (Thesis advisor) / Hedman, Kory W (Thesis advisor) / Askin, Ronald G. (Committee member) / Mirchandani, Pitu (Committee member) / Arizona State University (Publisher)
Created2015
Description
This dissertation carries out an inter-disciplinary research of operations research, statistics, power system engineering, and economics. Specifically, this dissertation focuses on a special power system scheduling problem, a unit commitment problem with uncertainty. This scheduling problem is a two-stage decision problem. In the first stage, system operator determines the binary commitment status (on or off) of generators in advance. In the second stage, after the realization of uncertainty, the system operator determines generation levels of the generators. The goal of this dissertation is to develop computationally-tractable methodologies and algorithms to solve large-scale unit commitment problems with uncertainty.
In the first part of this dissertation, two-stage models are studied to solve the problem. Two solution methods are studied and improved: stochastic programming and robust optimization. A scenario-based progressive hedging decomposition algorithm is applied. Several new hedging mechanisms and parameter selections rules are proposed and tested. A data-driven uncertainty set is proposed to improve the performance of robust optimization.
In the second part of this dissertation, a framework to reduce the two-stage stochastic program to a single-stage deterministic formulation is proposed. Most computation of the proposed approach can be done by offline studies. With the assistance of offline analysis, simulation, and data mining, the unit commitment problems with uncertainty can be solved efficiently.
Finally, the impacts of uncertainty on energy market prices are studied. A new component of locational marginal price, a marginal security component, which is the weighted shadow prices of the proposed security constraints, is proposed to better represent energy prices.
In the first part of this dissertation, two-stage models are studied to solve the problem. Two solution methods are studied and improved: stochastic programming and robust optimization. A scenario-based progressive hedging decomposition algorithm is applied. Several new hedging mechanisms and parameter selections rules are proposed and tested. A data-driven uncertainty set is proposed to improve the performance of robust optimization.
In the second part of this dissertation, a framework to reduce the two-stage stochastic program to a single-stage deterministic formulation is proposed. Most computation of the proposed approach can be done by offline studies. With the assistance of offline analysis, simulation, and data mining, the unit commitment problems with uncertainty can be solved efficiently.
Finally, the impacts of uncertainty on energy market prices are studied. A new component of locational marginal price, a marginal security component, which is the weighted shadow prices of the proposed security constraints, is proposed to better represent energy prices.
ContributorsLi, Chao (Author) / Hedman, Kory W (Thesis advisor) / Zhang, Muhong (Thesis advisor) / Mirchandani, Pitu B. (Committee member) / Wu, Teresa (Committee member) / Arizona State University (Publisher)
Created2016
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