OPTIMAL MANAGEMENT AND CONTROL OF RENEWABLE ENERGY BASED GENERATION RICH INTEGRATED TRANSMISSION AND DISTRIBUTION ELECTRIC GRID

Renewable energy resources advancement and offerings are steadily increasing, a major factor leading to its global fast adoption. The connection of these resources to the electric grid, however, needs to be studied to ensure efficiency both from an operational and regulatory standpoint. The IEEE 1547 has been used to establish standards for grid interconnection of some renewable energy resources (RERs). In this dissertation, the operations of RERs connected to the grid with respect to their control, management, and optimization are studied. It is of note that RERs are intermittent in nature and this can have effects on the power quality metrics or utility objectives on either network separately or collectively. For instance, the stability of the grid can be affected due to the low inertia of these resources, which can impact the voltage or the grid frequency. A novel adaptive controller was developed to damp the oscillations caused by these RERs, the controller was initially tested with RERs in one network architecture, and it offers advantages such as dynamically responsive support to the grid to control the frequency, a frequency spectrum was used to determine the amount of support required in an adaptive manner. The architecture was then expanded to a network model that has both transmission and distribution networks integrated together with the interconnection of multiple RERs connected to the grid, the capabilities of the proposed architecture were evaluated with different test cases with different grid events. The architecture had the capability to control multiple generators as well as damp the oscillations observed during the test cases and simulations performed, by adaptively updating the gain of the power system stabilizers (PSS).
On the management side, A new technique was developed with grid-connected RERs that provide real-time visibility of two integrated networks during operation. Presently, the operations don't offer such capabilities as the transmission system operator (TSO) is often times blind to the distribution system operator (DSO). Our technique makes it possible for the transmission network to adjust itself in real-time in case of sudden changes in the distribution network with RERs connected, A stochastic linear optimization technique; Linear decision rule (LDR) that establishes the relationship between the generators in the transmission network and the RERs in the distribution network was implemented, the technique addresses one of the major issues with integrated T&D networks which is boundary mismatch caused by the reverse power flow from the distribution network, in addition to offering the operational advantages required by most utilities like minimization of voltage deviation, and minimization of cost of operation, as it eliminates the need for curtailing RERs which is the current implementation used by most utilities, the technique theorem proof was also discussed. Furthermore, Grid Connected RERs are multiperiod in nature, it is therefore imperative to study their behavior at each time interval, the optimization framework was extended to such studies to handle the reverse power flow operation due to the irradiance daily curve, and the optimal power flow formulation was transformed to multiperiod optimal power flow MPOPF. The effectiveness of the proposed architecture was tested with an irradiance curve, and a typical residential load curve, it demonstrated the capability to reduce the boundary mismatch while ensuring the grid objectives for each network were achieved. Finally, the impact of electric vehicle charging was studied and a management approach was developed, Electric vehicles (EVs) adoption is also increasing impacts of the distribution network on the transmission network with respect to grid penetration, we developed a two-stage stochastic linear optimization in the integrated T&D to handle the uncertainty with electric vehicle charging and compared with effective EV charging management technique that was developed.