Development of system separation strategy using synchrophasor data

In the current market system, power systems are operated at higher loads for economic reasons. Power system stability becomes a genuine concern in such operating conditions. In case of failure of any larger component, the system may become stressed. These events may start cascading failures, which may lead to blackouts. One of the main reasons of the major recorded blackout events has been the unavailability of system-wide information. Synchrophasor technology has the capability to provide system-wide real time information. Phasor Measurement Units (PMUs) are the basic building block of this technology, which provide the Global Positioning System (GPS) time-stamped voltage and current phasor values along with the frequency. It is being assumed that synchrophasor data of all the buses is available and thus the whole system is fully observable. This information can be used to initiate islanding or system separation to avoid blackouts. A system separation strategy using synchrophasor data has been developed to answer the three main aspects of system separation: (1) When to separate: One class support machines (OC-SVM) is primarily used for the anomaly detection. Here OC-SVM was used to detect wide area instability. OC-SVM has been tested on different stable and unstable cases and it is found that OC-SVM has the capability to detect the wide area instability and thus is capable to answer the question of “when the system should be separated”. (2) Where to separate: The agglomerative clustering technique was used to find the groups of coherent buses. The lines connecting different groups of coherent buses form the separation surface. The rate of change of the bus voltage phase angles has been used as the input to this technique. This technique has the potential to exactly identify the lines to be tripped for the system separation. (3) What to do after separation: Load shedding was performed approximately equal to the sum of power flows along the candidate system separation lines should be initiated before tripping these lines. Therefore it is recommended that load shedding should be initiated before tripping the lines for system separation.

STATE OF CHARGE BALANCING DROOP CONTROL

This thesis presents a load sharing method applied in a distributed micro grid system. The goal of this method is to balance the state-of-charge (SoC) of each parallel connected battery and make it possible to detect the average SoC of the system by measuring bus voltage for all connected modules. In this method the reference voltage for each battery converter is adjusted by adding a proportional SoC factor. Under such setting the battery with a higher SoC will output more power, whereas the one with lower SoC gives out less. Therefore the higher SoC battery will use its energy faster than the lower ones, and eventually the SoC and output power of each battery will converge. And because the reference voltage is related to SoC status, the information of the average SoC in this system could be shared for all modules by measuring bus voltage. The SoC balancing speed is related to the SoC droop factors. This SoC-based load sharing control system is analyzed in feasibility and stability. Simulations in MATLAB/Simulink are presented, which indicate that this control scheme could balance the battery SoCs as predicted. The observation of SoC sharing through bus voltage was validated in both software simulation and hardware experiments. It could be of use to non-communicated distributed power system in load shedding and power planning.

Design, construction, and testing of a research quality electric machine testbed

With energy demands and costs growing every day, the need for improving energy efficiency in electrical devices has become very important. Research into various methods of improving efficiency for all electrical components will be a key to meet future energy needs. This report documents the design, construction, and testing of a research quality electric machine dynamometer and test bed. This test cell system can be used for research in several areas including: electric drives systems, electric vehicle propulsion systems, power electronic converters, load/source element in an AC Microgrid, as well as many others. The test cell design criteria, and decisions, will be discussed in reference to user functionality and flexibility. The individual power components will be discussed in detail to how they relate to the project, highlighting any feature used in operation of the test cell. A project timeline will be discussed, clearly stating the work done by the different individuals involved in the project. In addition, the system will be parameterized and benchmark data will be used to provide the functional operation of the system. With energy demands and costs growing every day, the need for improving energy efficiency in electrical devices has become very important. Research into various methods of improving efficiency for all electrical components will be a key to meet future energy needs. This report documents the design, construction, and testing of a research quality electric machine dynamometer and test bed. This test cell system can be used for research in several areas including: electric drives systems, electric vehicle propulsion systems, power electronic converters, load/source element in an AC Microgrid, as well as many others. The test cell design criteria, and decisions, will be discussed in reference to user functionality and flexibility. The individual power components will be discussed in detail to how they relate to the project, highlighting any feature used in operation of the test cell. A project timeline will be discussed, clearly stating the work done by the different individuals involved in the project. In addition, the system will be parameterized and benchmark data will be used to provide the functional operation of the system.

Minimizing residential distribution system operating costs through intelligently scheduled plug-in hybrid electric vehicle charging

Rising fuel prices and environmental concerns are threatening the stability of current electrical grid systems. These factors are pushing the automobile industry towards more effcient, hybrid vehicles. Current trends show petroleum is being edged out in favor of electricity as the main vehicular motive force. The proposed methods create an optimized charging control schedule for all participating Plug-in Hybrid Electric Vehicles in a distribution grid. The optimization will minimize daily operating costs, reduce system losses, and improve power quality. This requires participation from Vehicle-to-Grid capable vehicles, load forecasting, and Locational Marginal Pricing market predictions. Vehicles equipped with bidirectional chargers further improve the optimization results by lowering peak demand and improving power quality.

AGENT BASED, DISTRIBUTED CONTROL STRATEGIES AND OPTIMIZATION OF PLUG-IN ELECTRIC VEHICLES ON SMART/MICROGRID ARCHITECTURES

This thesis will present strategies for the use of plug-in electric vehicles on smart and microgrids. MATLAB is used as the design tool for all models and simulations. First, a scenario will be explored using the dispatchable loads of electric vehicles to stabilize a microgrid with a high penetration of renewable power generation. Grid components for a microgrid with 50% photovoltaic solar production will be sized through an optimization routine to maintain storage system, load, and vehicle states over a 24-hour period. The findings of this portion are that the dispatchable loads can be used to guard against unpredictable losses in renewable generation output. Second, the use of distributed control strategies for the charging of electric vehicles utilizing an agent-based approach on a smart grid will be studied. The vehicles are regarded as additional loads to a primary forecasted load and use information transfer with the grid to make their charging decisions. Three lightweight control strategies and their effects on the power grid will be presented. The findings are that the charging behavior and peak loads on the grid can be reduced through the use of distributed control strategies.