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Showing papers by "K. Shanti Swarup published in 2021"


Journal ArticleDOI
TL;DR: Research gaps and future directions on all these applications in smart grids, such as the impact of communication failures or communication delays, effect of uncertainties, incorporating the fraudulent behaviour of participants, usage of Blockchain technology, implementation of smart contract on the performance of these algorithms are presented.
Abstract: Optimization algorithms play a significant role in the optimal solution of various problems in Smart Grid. Distributed algorithms are of considerable research interest as these algorithms replace the centrally computed algorithms. This paper surveys the literature on the Alternating Direction Method of Multipliers (ADMM) algorithm, its versions and their applications in Smart grid operation and control, particularly in Optimal Power Flow (OPF), Economic Dispatch (ED), Demand Response (DR), pricing mechanism, Electric Vehicles (EVs), Cyber–Physical Systems (CPS), Multi Energy Systems (MES), Peer to Peer (P2P) trading, resilience, forecast techniques, State Estimation (SE), and some miscellaneous topics. This paper presented research gaps and future directions on all these applications in smart grids. Furthermore, it provided a joint research gap on all these applications, such as the impact of communication failures or communication delays, effect of uncertainties, incorporating the fraudulent behaviour of participants, usage of Blockchain technology, implementation of smart contract on the performance of these algorithms. Furthermore, the work has developed a decentralized and distributed automatic electricity trading mechanism for the independent microgrid without needing a central aggregator.

15 citations


Posted Content
TL;DR: In this paper, a pioneer work on learning optimal bidding strategies from observation and experience in a three-stage reactive power market is reported, where a bidding strategy is to be learned from market observations and experience of imperfect oligopolistic competition-based markets.
Abstract: In real time electricity markets, the objective of generation companies while bidding is to maximize their profit. The strategies for learning optimal bidding have been formulated through game theoretical approaches and stochastic optimization problems. Similar studies in reactive power markets have not been reported so far because the network voltage operating conditions have an increased impact on reactive power markets than on active power markets. Contrary to active power markets, the bids of rivals are not directly related to fuel costs in reactive power markets. Hence, the assumption of a suitable probability distribution function is unrealistic, making the strategies adopted in active power markets unsuitable for learning optimal bids in reactive power market mechanisms. Therefore, a bidding strategy is to be learnt from market observations and experience in imperfect oligopolistic competition-based markets. In this paper, a pioneer work on learning optimal bidding strategies from observation and experience in a three-stage reactive power market is reported.