Research Areas

(1) Computational Intelligence Techniques for Modeling and Control of Smart Grid
Original research in the field of computational intelligence techniques formodeling and control of distributed energy systems has been carried out. This research intends to create a framework for the introduction of distributed power generation in the context of smart grid. We investigate system security, reliability and planning issues pertaining to embedded distributed generators, from which the local utilities and interested parties can benefit.

(2) Dynamic Optimization and Management of Micro Grid
This research aims to develop intelligent computational tools for efficiency optimization and management of smart micro grids, and conduct hardware in the loop testing.  In this work, we design, develop, and deploy innovative technologies for the next generation of smart micro-grids to provide solutions to the related problems to make the grid more adaptable, flexible, intelligent and robust. For maximum utilization of renewable energy, new energy management controllers and optimization tools are being developed to provide efficient control and supply the energy demand of all loads (critical and non-critical). The dynamic energy management system performs real-time dynamic optimization of energy generated in a micro-grid system through intelligent energy dispatch.

(3) Intelligent Mobility Modeling and Management
In order to support a wide range of urban transportation options, mobility management becomes a crucial factor when designing infrastructure for urban transport networks. Due to the anticipated increase in the number of vehicles in the future, the networks should be able to support a huge number of users and their individual requirements while at the same time optimizing the resources. This research in intelligent mobility management system aims to develop new decision making technologies in the field of hybrid intelligent systems which aid the vehicle scheduling, intelligent congestion-aware routing and energy management. A new architecture that applies hybrid computational intelligence concepts to implement a cooperative multi-agent system for real-time traffic signal control of a complex traffic network has also been developed. The large-scale traffic signal control problem is divided into various sub-problems and modularized, hierarchical agent architecture have been used. Coordinated control is achieved by intelligent agents, which adopt the cooperative distributed problem solving approach.

(4) Integration of Distributed Energy Resources

Distributed energy resources (DER) are smaller power sources that can be aggregated to provide power necessary to meet regular demand. As the electricity grid continues to modernize, distributed energy resources such as solar PV, wind, storage and advanced renewable technologies can help facilitate the transition to a smarter grid. On the other hand, their presence also makes the systems more complex, and difficult to manage and operate due to variable and uncertain generation, stochastic load profiles and power flows, and their wide geographical distribution. Deploying DERs in a widespread, efficient and cost-effective manner requires complex integration with the existing electricity grid. This research aims to identify and resolve the above challenges of integration, and facilitate a smoother transition for the electricity industry and their customers into the next age of electricity infrastructure. Quantifying the suboptimal decisions due to various uncertainties is an important step in operation planning for Smart Grid applications. This research also involves generation and load forecasting for systems with large scale DER deployment, which is essential for realization of dynamic energy management systems for future Smart Grids.

(5) Evolutionary Computation for Optimization and Scheduling

Practical problems are often characterized by the existence of multiple, sometimes conflicting, objectives and criteria. In this research, we are developing evolutionary algorithms, hybridized with heuristic approaches for solving real world problems with large number of objectives in constrained environments. Various types of operators and control parameter settings that are likely to produce the best results are also being investigated. 

My research has also involved exploration of several other techniques nature-inspired techniques for learning and optimization in dynamic and rapidly evolving environments. These systems are being developed and tested for distributed energy systems.

(6) Demand Response and Management

The future Smart Grid will allow network access to grid operators to coordinate and control the loads of participating customers, and shift the load to off-peak hours to better utilize power generation resources. This research aims to investigate various Demand Response (DR) and Demand Side Management (DSM) strategies to accommodate stochastic customer demand through more efficient use of power generation resources. The role of electrical vehicles as a mobile load and distributed energy source is investigated and their role in supporting the grid for demand side management, and reliability enhancement is explored.

(7) Multi-agent System for Operation of Smart-Grid

Intelligent agents and multi-agent systems offer a particularly attractive approach for the design and implementation of complex, flexible, and scalable information systems. I have been working on the design of multi-agent systems that deliver high levels of cooperation and reliability in dynamic and rapidly evolving Smart Grid environment. Such multi-agent systems that coordinate intelligently to optimize system-wide objectives offer interesting possibilities for operation optimization, decision making and control of a Smart Grid if they are coordinated in a distributed control paradigm together with decentralization of information. We have developed a scalable multi-agent simulator to simulate the operation of various entities in the power system. The decision making, control, and management tools are developed and tested on a lab-scale Smart Grid which has power electronics components, SCADA, controllers, renewable energy sources, energy storage, and linear and controllable loads. A real-time simulation of such interconnected micro-grids is carried out on the real-time digital simulator (RTDS) to study the operation of multiple micro-grids on the distribution system.


  • Home
  • Research Areas