Chandlekar Sanjay Rajendrabhai supervised by Dr. Sujit Gujar received  his doctorate in  Computer Science and Engineering (CSE). Here’s a  summary of his research work on AI-Based Autonomous Broker for Smart Grids: Theory, Design and Practice: 

The emergence of AI-driven systems has transformed smart grid networks, enabling widespread automation in decision-making. A smart grid is an advanced electricity distribution system that empowers customers to actively participate through smart meters. These grids operate across three key markets: wholesale, tariff, and balancing markets. In this ecosystem, distribution companies, or electricity brokers, play a pivotal role by procuring electricity in the wholesale market through double auctions and selling it to customers in the tariff market. Meanwhile, the balancing market oversees real-time supply and demand, penalizing brokers responsible for imbalances. The primary goal of brokers is to maximize profits, which involves addressing three critical challenges: (i) minimizing procurement costs in the wholesale market, (ii) offering competitive yet profitable tariffs in the tariff market, and (iii) mitigating peak demand scenarios to avoid penalties. Achieving this requires brokers to develop intelligent strategies leveraging AI techniques. This thesis develops AI-based strategies that enhance the efficiency of electricity brokers, tested using a close-to-real-world simulation platform, PowerTAC. Specifically, we design various bidding strategies for brokers operating in the wholesale market, where electricity is traded through day-ahead periodic double auctions (PDAs). The first strategy draws inspiration from game theory, leveraging Nash equilibrium principles for a single-buyer and single-seller setup. This is extended to real-world scenarios consisting of multiple buyers and sellers using reinforcement learning techniques. The second strategy reduces procurement costs by exploiting the supply curve information of prominent sellers to inform bidding decisions. The third strategy employs Markov Perfect Nash Equilibrium (MPNE) policies to model buyer behavior with known supply curves and introduces an algorithm to address scenarios where supply curve information is unavailable. The fourth strategy leverages Monte Carlo Tree Search (MCTS) to operate in the continuous action space of bid prices for optimized bidding in PDAs. In the tariff market, we develop strategies for generating attractive tariff contracts to build and retain a robust customer base. Using game theory, we demonstrate that maintaining an optimal market share significantly boosts net revenue. To achieve this, we propose both heuristic and learning-based approaches, with the latter employing multi-armed bandit (MAB) techniques for tariff generation. To address peak demand scenarios, we propose a demand response mechanism that incentivizes customers to shift their electricity usage away from peak hours. We present an optimal algorithm for allocating discounts that maximize expected peak demand reduction while adhering to budget constraints. Additionally, we introduce an MAB-based online algorithm to handle cases where customer responses to incentives are initially unknown. Finally, we integrate these strategies to develop our autonomous broker, VidyutVanika, for the PowerTAC simulation-based tournament. VidyutVanika competed against other brokers with the objective of maximizing profits. The results from the PowerTAC 2021 and 2022 tournaments demonstrate the effectiveness of our approach, with VidyutVanika emerging as the champion in both years.

 May 2025