Projects Work
Projects Work
Research Topic: Autonomous Open Energy Communities: A New Paradigm for Power Grid Operations (AUTONOMY)
PI: Dr. Barry Hayes
AUTONOMY proposes the world’s first autonomous community energy management system, demonstrating the advantages of an electricity grid supported by autonomously-operated, decentralized cells over present centralized command and control approaches. It pioneers a novel transfer learning approach to exchange expert knowledge amongst energy manager agents in different communities across the grid, as part of a wider strategy to decentralize grid energy balancing and coordinate multiple energy resources at the local level.
Research Topic: ENFLEX CORTEX: ENOWA –Flexibility Platforms (FP): Modelling & Simulation and Evaluation and Lab-Testing for NEOM’s future Digital Energy Platform (DEP) at Saudi Arabia
PI: Dr. Charalambos Konstantinou
As a part of post-doc research work, I worked on the GENFLEX project, which focuses on demand flexibility development for NEOM city in Saudi Arabia. It aims to design a planning and operational framework for the flexibility market. It utilizes various flexible resources to participate in the local and wholesale market and support the flexibility of the grid operator. A part of the work, a flexibility framework consisting of hydrogen electrolyzers, PV, and BESS was developed to induce demand flexibility to the grid. Besides, I also worked in the area of cybersecurity attack on demand response. The cybersecurity attack is characterized by the malicious modification of data information of power systems using cyberspace, which subsequently affects the techno-economics of the systems. I use a false data injection (FDI) attack on the DR price signal to investigate its impact on distribution systems (DS).
Thesis Topic: Modeling and Strategies of Demand Response in Contemporary Distribution Systems
Advisor: Prof. Nikhil Gupta
My PhD research presents DR (Demand Response) modeling and its operational strategies in the contemporary DS.
It proposes the various DR modeling approaches considering the customers’ behavioral attributes in addition to DS characteristics. The proposed DR models are adapted from consumer theory and microeconomics and are suitably redesigned for a pragmatic DR representation. DR models are developed using price elasticity model (PEM) and utility functions (UFs). These price- and incentive based DR models have been developed from the DS’s perspective, considering load diversity, load patterns, price cross-subsidy, and DR/customers’ attributes (i.e., adaptability, adjustability, load recovery, willingness, risk behaviour, etc.,). Moreover, DR operational strategies are developed considering DS characteristics such as the different customer classes, price cross-subsidy, load patterns, load diversity, etc. It is proposed for both (PB and IB) frameworks with the different stakeholders under the DR environment. The strategic interactions among the various stakeholders such as the distribution system operator (DSO), demand response providers (DRPs), and customers, are devised using game theory. Further, the operational frameworks are optimized and solved using customized and improved decomposition-based optimization approaches. The proposed frameworks and solution algorithms are applied on the standard and practical distribution systems considering ac network constraints in addition to pragmatic DR and DS attributes.
Thesis Topic: Dynamic Economic Load Dispatch with Renewable Energy Sources using an Improved Fireworks Algorithm
Advisor: Prof. Nikhil Gupta
In my master’ research, I have worked on the dynamic economic load dispatch (DELD) problem considering renewable energy resources using Improved Firework algorithm (IFWA). The DELD is an extension of the economic load dispatch problem (ELD), which optimally schedules the generators for meeting the demand while satisfying generator constraints. The DELD problem is a highly non-linear, non-convex, multi-constraint optimization problem with continuous decision variables. Therefore, an Improved Fireworks Algorithm (IFWA), an improved version of the standard Fireworks Algorithm, is utilized to solve the problem. The FWA is a swarm-based computational intelligent technique. It is inspired by the explosion of the fireworks and is extensively found to be a powerful technique among all other metaheuristic techniques. However, the conventional FWA faces ineffectiveness for the functions having optima far away from the origin as exists in DELD problem. Therefore, several modifications have been suggested in FWA, and a new method, Improved Fireworks Algorithm using Chaotic Sequence Operator (IFWA-CSO) is proposed. The validation and effectiveness of the FWA and developed variants are first tested on some well-known mathematical benchmark functions and then applied to the power system optimization problem of economic operation in static and dynamic states with a variety of constraints. The obtained numerical results verify the efficacy of the proposed method and its variants and seem to be a more promising optimizer than conventional FWA.