Smart Grid Demonstration: Distributed Active and Reactive Power Control
Authors: Siddarth V. Rajamani, Matthew Aberman, Zhihua Qu
ABSTRACT: This is an experimental setup for communication and control for the Smart Grid, developed at University of Central Florida, USA in partnership with Texas Instruments Incorporated, USA. Figure 1 shows a high-level setup of the Energy Grid with Distributed Energy Resources (DER) to form a micro-grid. The system is made up of three different loads (Load 1, Load 2 and Load 3) at locations marked Location 1, Location 2 and Location 3. These are variable inductive and capacitive load-units that can be changed intermittently. In the current setup, Load 2 is an adjustable AC load bank, either inductive or capacitive; Load 1 and Load 3 are constant impedance loads. These loads in the micro-grid vary arbitrarily and the quantity for active and reactive power fluctuates. The primary objective is to minimize the amount of reactive power supplied from the main-grid. The approach is to use distributed inverters with the aid of multiple local communication channels for reactive power compensation of the micro-grid real-time in a distributed and co-operative manner. The test bed is flexible and n number of load locations can be added while the distributed measurements are obtained with ease.
An Adaptive Restorative Method for Resilient Power Distribution Networks
Authors: Ranadhir Sarkar, Azwirman Gusrialdi, Zhihua Qu
In this poster, we propose a restorative method to minimize the unserved demand in a power distribution network due to line outage. The restoration is achieved by tie-line switching and by solving a mixed-integer non-linear problem. By exploiting the information on pre-contingency solution, topology together with post-contingency line congestion requirements and in combination with an improved greedy search algorithm, a method is proposed which reduces the search space of the combinatorial problem. Case studies on IEEE 33-bus distribution network is presented to illustrate the results and validate the performance of the proposed method.
Distributed Predictive Control of Data-Driven Demand Response in Smart Grid
Authors: Hamed Valizadeh Haghi, Zhihua Qu
This research is part of a broader perspective on developing high-performance analytics, including predictive analytics, to improve distributed control strategies for smart grid software. This would enable utilities to become more proactive in decision-making, adjusting their strategies based on predictive views into the future, thus allowing them to side-step problems and capitalize on the smart grid technologies, such as energy storage, that are now being deployed at scale. The following objectives are pursued: (1) Spatio-temporal adaptive modeling of data (load, wind and solar); (2) Predictive data analytics and forecasting; (3) Data-driven distributed control of active and reactive powers; (4) Integration of energy storage systems. Active and reactive power are coordinated to provide voltage support and demand response. Optimization and control mechanisms consist of two levels: instantaneous power/voltage control and temporal parameter adjustment and storage control. By imposing measures of the most likely ensembles, a distributed method is carried out for cooperative control of the renewable generation and energy storage. Obtained solutions can reflect on the system requirements for the upcoming times along with the instantaneous cooperation between distributed resources.
An Integrated Optimization and Control Algorithm for Distributed Demand Responses in Smart Grid
Authors: Towfiq Rahman, Zhihua Qu
In this work, an integrated algorithm of optimization and control is proposed to enable smooth and autonomous Demand Response (DR) for controllable loads. Controllable loads (which include heating ventilating and air conditioning units, electric vehicles, etc.) may have heterogeneous dynamics and different operational constraints themselves, but they can be used to vary load demand and contribute towards primary frequency control. An algorithm is proposed that senses local frequency deviation and adaptively maintains the balances of active power generation and load consumption across the whole grid (or within a microgrid) while keeping the physical parameters of controllable loads within consumer comfort range. It is shown that, the proposed algorithm automatically and distributively finds the optimal solution for DR, maintaining stable system frequency and also ensuring consumer comfort in the process. Simulation results are included to illustrate the analytical analysis.
Developing an Adaptive Self-healing Decision Support Tool for Bulk Power System Using WAMS Data
Authors: Amir Golshani, Wei Sun
Power outages cost American business billions of dollars and jeopardize the lives of hospital patients. The loss can be greatly reduced with a fast, reliable and flexible self-healing action. The existing approaches provide offline self-healing actions which prolong the system recovery time. This work proposes an online adaptive self-healing decision support system that adapt to constantly changing system conditions. On the other hand, as the penetration of renewable sources increase, various challenges will be imposed on the power system self-healing process. We present a two-stage Mixed Integer Linear Programming (MILP) decision making model including static and dynamic constraints which can be solved using stochastic optimization approach. Our model incorporates the state-of-art Wide-area Monitoring System (WAMS) technology. Standard test systems, IEEE 39-bus, has been studied to verify the effectiveness of the proposed model.