Data-Driven Operation and Control of Active Power Distribution Systems with High Penetration of Distributed Energy Resources and Energy Storage 2018- Current
This project aims to develop: a) a novel Receding Horizon Control (RHC) based mixed-integer second-order cone programming (MISOCP) model for optimal power flow in power distribution systems that can scale up to integrate thousands of aggregated nodes and provide set points (integer or real) for passive and active devices considering unbalanced distribution system operation, b) a stochastic model predictive consensus framework for distributed modeling of subnetworks in power distribution system with large number of active devices, c) a secondary control framework that provides improved active/reactive power control and can ensure seamless integration with vendor driven controllers, in turn, enhancing power quality and stability, and d) an implementation platform including communication loops with real-life data and models from the local utility that proves feasibility of the methodology. The proposed optimization framework can provide global solutions for decision control variables and set points, including switches and transformer taps, at all active nodes in the power distribution system. Also, the methodology can incorporate stochastic or deterministic changes in the devices such as DERs and energy storage, considering each subnetwork. Moreover, the architecture can be seamlessly integrated with the existing vendor driven controllers thus capable of accommodating high in-feed of distributed resources and providing a low-cost solution to the exponential increase in decision and grid state variables. Supported by NSF.
Integration and Control of Energy Storage with Renewable Energy Devices to improve dispatchability 2013- Current
Current power grid provides very low reliability when integrated with high penetration of renewable energy resources. To integrate high penetration of renewable energy resources energy storage options are mandatory. However, state-of-the-art research is not able to control battery energy storage with multiple functions. Research addresses this gap and finds out methods to integrate stacked energy storage algorithms. The main body of knowledge developed under this work include 1) Designing outer loop controllers for battery energy storage 2) Developing algorithms for multiple functions of energy storage including renewable energy capacity firming, energy arbitrage and voltage regulation. 3) Developing test-bed platform and evaluate real-time implementation issues 4) Field testing the algorithm and provide optimization functions for value-based control. This research is the first to address and provide real-time solutions for energy storage applications for multiple functions. The outcome of the research will provide a framework that can integrate renewable energy resources with multiple storage control based on grid value proposition. Supported by Duke Energy.
Wide-Area Monitoring and Control with Renewable Energy Integration 2014- Current
The main objective of this research is to understand the need and design methodology for wide area controllers. The need for evaluating such architecture based on prediction and real-time measurements is a critical factor in managing stability of modern power grid integrated with renewable energy resources. The main body of knowledge developed under this work includes 1) Designing wide area controllers considering learning and prediction 2) Analyzing various aspects of power system control problems with the proposed concept. 3) Developing test-bed platform and evaluate real-time implementation issues 4) Analyzing the system impact with wide area control architecture considering oscillation decay conditions with respect to communication delays. This research is the first to address the dynamic wide area control architecture considering value prioritization and learning. Also, this research is the first in developing distributed wide area control approaches with distributed coherency and distributed control. The main advantages of this algorithm are that it is feasible, faster, and more reliable than global and local controllers acting alone. Also, real-time implementation and distributed wide area control applications are established by which renewable energy resources can be integrated. Current research includes oscillation monitoring, hybrid measurement, and model-based distributed wide area control. Supported by NSF and Utilities.
Interdependent Energy System Modeling and Optimal Control for Renewable Energy Integration at the Power Distribution and Transmission System 2018- Current
The main objective of this project is to develop a transmission-distribution dynamic and real-time implementable model that can be used for evaluating the effect of renewable energy penetration on the power grid at both the transmission and distribution level. The model is also used to evaluate the system dynamics, measurement integrated analysis such as state estimation, distributed wide area monitoring, stability, system reliability and ability to have increased penetration of renewables in the grid. The model framework includes all system dynamics and control options including the models for EMS and DMS. The approach is to utilize real-time simulators, Phasor Measurement Units, control framework, measurement stream, and high-performance computing. The models are then used for model validation, control, optimal energy management, demand and supply energy management options, integration of distributed energy resources, provide time-scale separated management and control, data science and analysis and effect of integrating digital devices on to the grid. Supported internally and through industry grants.
Intelligent Optimization and Control of Wind-farm Flywheel for Larger Grid Penetration 2010- Current
In this project, an intelligent optimization model for a wind turbine is developed which monitors and predict wind profile in a cluster of the wind farm. This mechanical model is then integrated to the electrical machine model that optimize and predict the optimal power at normal and abnormal operating conditions. The main objective of this project is to develop wind farm control that increases its penetration to the mega grid. The uniqueness of the project is the integration of fully fledged mechanical and electrical model with intelligent algorithms. The approach is used for control of wind farms for improving grid reliability and flexibility. The work is supported by industry grants and UNCC.
Previous Completed Projects (Selected)
A Novel Intelligent Grid Optimization Architecture Using Hierarchical Multi-Agent Framework for Modern Sustainable Power Grid: 2013- 2017
Power Grid Optimization and Control with Hybrid and Intelligent Agent Architecture: 2008- 2014
Micro-grid Power Module using Hybrid Renewable Energy Fuels: 2008- 2012
Scalable Intelligent Algorithms for Distributed Power System Control and Optimization: 2004- 2008
Deep Learning Based Adaptive Neuro-Controller: 2000- 2004
A Neural Network Approach to Voltage Stability Assessment and Improvement:1998-2004