Text

theses

Significant increases in energy prices and price volatility, worsening global warming, adverse environmental footprints of fossil fueled energy and recent advances in energy system technologies have significantly elevated interest in clean distributed energy resources (DERs) and energy storage. Over the past decade, various forms of DERs, such as combined heat & power, fuel cells, hybrid power systems, microturbines, photovoltaic systems and reciprocating engines have been successfully integrated to the electric distribution systems. Moreover, environmental concerns have been urging for more and more integration of distributed renewable energy resources into the overall energy infrastructure. However, many challenges still remain; for instance, renewable generation (such as wind and solar) is not dispatchable and its production is not necessarily coincident with system demand. As a result, cheap renewable may not efficiently be utilized at all times. In this context, battery energy storage systems can provide operation flexibility by storing excess renewable energy when there is low demand and dispatching it when its needed the most. With this in mind, the Federal Energy Regulatory Commission (FERC) recently enacted FERC Order 841, which attempts to remove barriers to the participation of electric storage resources in the capacity, energy, and ancillary service markets operated by Regional Transmission Organizations (RTO) and Independent System Operators (ISO).
In this work, a Distributed Network-Aware Planning and Control System is developed aiming at optimal sizing, capacity allocation and planning and control of energy storages using real-time information. A storage node in such network can be a single functional unit or an aggregation of multiple units (e.g. modular network of energy storages) owned by either a utility company or by a third party. Capacity of storage nodes in the network can be static and deterministic or change dynamically due to units’ degradation and/or unavailability. For instance, a parking facility with EVs and Vehicle to Grid (V2G) charging stations can be a good example of an aggregate storage node. Arrival and departure of vehicles to this facility, permission for V2G by vehicle owners, and vehicles’ scheduling and charging requirements all together define a complex stochastic process that govern the overall capacity of the facility’s energy storage. We build a model that describes such a process and determines energy storage capacity of the aggregate node. The underlying model closely connects to business opportunities that such a facility can present to individual vehicle owners or to the facility operator.
The planning and control scheme, introduced in this dissertation has significant impacts on overall energy network performance and efficiency by balancing dynamic demands with energy supply, and can be utilized to address a range of energy storage applications including power quality and network reliability. The undertaken research in this dissertation can provide guidance on DER and energy storage operation and maintenance (O&M) strategies which can be utilized as a means for supporting microgrid operators, regulators and utility capacity planners towards strategic planning decisions.

ETD_9568

Ph.D.

Includes bibliographical references

doi:10.7282/t3-n7jp-5227

ETD doctoral

The author owns the copyright to this work.