Matching Items (6)
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
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 thesis concerns the impact of energy storage on the power system. The rapidly increasing integration of renewable energy source into the grid is driving greater attention towards electrical energy storage systems which can serve many applications like economically meeting peak loads, providing spinning reserve. Economic dispatch is performed with bulk energy storage with wind energy penetration in power systems allocating the generation levels to the units in the mix, so that the system load is served and most economically. The results obtained in previous research to solve for economic dispatch uses a linear cost function for a Direct Current Optimal Power Flow (DCOPF). This thesis uses quadratic cost function for a DCOPF implementing quadratic programming (QP) to minimize the function. A Matlab program was created to simulate different test systems including an equivalent section of the WECC system, namely for Arizo-na, summer peak 2009. A mathematical formulation of a strategy of when to charge or discharge the storage is incorporated in the algorithm. In this thesis various test cases are shown in a small three bus test bed and also for the state of Arizona test bed. The main conclusions drawn from the two test beds is that the use of energy storage minimizes the generation dispatch cost of the system and benefits the power sys-tem by serving the peak partially from stored energy. It is also found that use of energy storage systems may alleviate the loading on transmission lines which can defer the upgrade and expansion of the transmission system.
ContributorsGupta, Samir (Author) / Heydt, Gerald T (Thesis advisor) / Vittal, Vijay (Committee member) / Ayyanar, Raja (Committee member) / Arizona State University (Publisher)
Created2012
Description
Electric power systems are facing great challenges from environmental regulations, changes in demand due to new technologies like electric vehicle, as well as the integration of various renewable energy sources. These factors taken together require the development of new tools to help make policy and investment decisions for the future power grid. The requirements of a network equivalent to be used in such planning tools are very different from those assumed in the development of traditional equivalencing procedures. This dissertation is focused on the development, implementation and verification of two network equivalencing approaches on large power systems, such as the Eastern Interconnection. Traditional Ward-type equivalences are a class of equivalencing approaches but this class has some significant drawbacks. It is well known that Ward-type equivalents "smear" the injections of external generators over a large number of boundary buses. For newer long-term investment applications that take into account such things as greenhouse gas (GHG) regulations and generator availability, it is computationally impractical to model fractions of generators located at many buses. A modified-Ward equivalent is proposed to address this limitation such that the external generators are moved wholesale to some internal buses based on electrical distance. This proposed equivalencing procedure is designed so that the retained-line power flows in the equivalent match those in the unreduced (full) model exactly. During the reduction process, accommodations for special system elements are addressed, including static VAr compensators (SVCs), high voltage dc (HVDC) transmission lines, and phase angle regulators. Another network equivalencing approach based on the dc power flow assumptions and the power transfer distribution factors (PTDFs) is proposed. This method, rather than eliminate buses via Gauss-reduction, aggregates buses on a zonal basis. The bus aggregation approach proposed here is superior to the existing bus aggregation methods in that a) under the base case, the equivalent-system inter-zonal power flows exactly match those calculated using the full-network-model b) as the operating conditions change, errors in line flows are reduced using the proposed bus clustering algorithm c) this method is computationally more efficient than other bus aggregation methods proposed heretofore. A critical step in achieving accuracy with a bus aggregation approach is selecting which buses to cluster together and how many clusters are needed. Clustering in this context refers to the process of partitioning a network into subsets of buses. An efficient network clustering method is proposed based on the PTDFs and the data mining techniques. This method is applied to the EI topology using the "Saguaro" supercomputer at ASU, a resource with sufficient memory and computational capability for handling this 60,000-bus and 80,000-branch system. The network equivalents generated by the proposed approaches are verified and tested for different operating conditions and promising results have been observed.
ContributorsShi, Di (Author) / Tylavsky, Daniel J (Thesis advisor) / Vittal, Vijay (Committee member) / Hedman, Kory (Committee member) / Ayyanar, Raja (Committee member) / Arizona State University (Publisher)
Created2012
Description
ABSTRACT
This dissertation introduces a real-time topology monitoring scheme for power systems intended to provide enhanced situational awareness during major system disturbances. The topology monitoring scheme requires accurate real-time topology information to be effective. This scheme is supported by advances in transmission line outage detection based on data-mining phasor measurement unit (PMU) measurements.
A network flow analysis scheme is proposed to track changes in user defined minimal cut sets within the system. This work introduces a new algorithm used to update a previous network flow solution after the loss of a single system branch. The proposed new algorithm provides a significantly decreased solution time that is desired in a real- time environment. This method of topology monitoring can provide system operators with visual indications of potential problems in the system caused by changes in topology.
This work also presents a method of determining all singleton cut sets within a given network topology called the one line remaining (OLR) algorithm. During operation, if a singleton cut set exists, then the system cannot withstand the loss of any one line and still remain connected. The OLR algorithm activates after the loss of a transmission line and determines if any singleton cut sets were created. These cut sets are found using properties of power transfer distribution factors and minimal cut sets.
The topology analysis algorithms proposed in this work are supported by line outage detection using PMU measurements aimed at providing accurate real-time topology information. This process uses a decision tree (DT) based data-mining approach to characterize a lost tie line in simulation. The trained DT is then used to analyze PMU measurements to detect line outages. The trained decision tree was applied to real PMU measurements to detect the loss of a 500 kV line and had no misclassifications.
The work presented has the objective of enhancing situational awareness during significant system disturbances in real time. This dissertation presents all parts of the proposed topology monitoring scheme and justifies and validates the methodology using a real system event.
This dissertation introduces a real-time topology monitoring scheme for power systems intended to provide enhanced situational awareness during major system disturbances. The topology monitoring scheme requires accurate real-time topology information to be effective. This scheme is supported by advances in transmission line outage detection based on data-mining phasor measurement unit (PMU) measurements.
A network flow analysis scheme is proposed to track changes in user defined minimal cut sets within the system. This work introduces a new algorithm used to update a previous network flow solution after the loss of a single system branch. The proposed new algorithm provides a significantly decreased solution time that is desired in a real- time environment. This method of topology monitoring can provide system operators with visual indications of potential problems in the system caused by changes in topology.
This work also presents a method of determining all singleton cut sets within a given network topology called the one line remaining (OLR) algorithm. During operation, if a singleton cut set exists, then the system cannot withstand the loss of any one line and still remain connected. The OLR algorithm activates after the loss of a transmission line and determines if any singleton cut sets were created. These cut sets are found using properties of power transfer distribution factors and minimal cut sets.
The topology analysis algorithms proposed in this work are supported by line outage detection using PMU measurements aimed at providing accurate real-time topology information. This process uses a decision tree (DT) based data-mining approach to characterize a lost tie line in simulation. The trained DT is then used to analyze PMU measurements to detect line outages. The trained decision tree was applied to real PMU measurements to detect the loss of a 500 kV line and had no misclassifications.
The work presented has the objective of enhancing situational awareness during significant system disturbances in real time. This dissertation presents all parts of the proposed topology monitoring scheme and justifies and validates the methodology using a real system event.
ContributorsWerho, Trevor Nelson (Author) / Vittal, Vijay (Thesis advisor) / Heydt, Gerald (Committee member) / Hedman, Kory (Committee member) / Karady, George G. (Committee member) / Arizona State University (Publisher)
Created2015
Description
Large-scale integration of wind generation introduces planning and operational difficulties due to the intermittent and highly variable nature of wind. In particular, the generation from non-hydro renewable resources is inherently variable and often times difficult to predict. Integrating significant amounts of renewable generation, thus, presents a challenge to the power systems operators, requiring additional flexibility, which may incur a decrease of conventional generation capacity.
This research investigates the algorithms employing emerging computational advances in system operation policies that can improve the flexibility of the electricity industry. The focus of this study is on flexible operation policies for renewable generation, particularly wind generation. Specifically, distributional forecasts of windfarm generation are used to dispatch a “discounted” amount of the wind generation, leaving a reserve margin that can be used for reserve if needed. This study presents systematic mathematic formulations that allow the operator incorporate this flexibility into the operation optimization model to increase the benefits in the energy and reserve scheduling procedure. Incorporating this formulation into the dispatch optimization problem provides the operator with the ability of using forecasted probability distributions as well as the off-line generated policies to choose proper approaches for operating the system in real-time. Methods to generate such policies are discussed and a forecast-based approach for developing wind margin policies is presented. The impacts of incorporating such policies in the electricity market models are also investigated.
This research investigates the algorithms employing emerging computational advances in system operation policies that can improve the flexibility of the electricity industry. The focus of this study is on flexible operation policies for renewable generation, particularly wind generation. Specifically, distributional forecasts of windfarm generation are used to dispatch a “discounted” amount of the wind generation, leaving a reserve margin that can be used for reserve if needed. This study presents systematic mathematic formulations that allow the operator incorporate this flexibility into the operation optimization model to increase the benefits in the energy and reserve scheduling procedure. Incorporating this formulation into the dispatch optimization problem provides the operator with the ability of using forecasted probability distributions as well as the off-line generated policies to choose proper approaches for operating the system in real-time. Methods to generate such policies are discussed and a forecast-based approach for developing wind margin policies is presented. The impacts of incorporating such policies in the electricity market models are also investigated.
ContributorsHedayati Mehdiabadi, Mojgan (Author) / Zhang, Junshan (Thesis advisor) / Hedman, Kory (Thesis advisor) / Heydt, Gerald (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Arizona State University (Publisher)
Created2017