Neural Fitted Q Iteration based Optimal Bidding Strategy in Real Time Reactive Power Market_1

TL;DR

This paper introduces a novel approach using neural fitted Q iteration to learn optimal bidding strategies in real-time reactive power markets, addressing the unique challenges posed by market conditions and rival behaviors.

Contribution

It presents the first method for learning optimal reactive power market bids from observations using neural fitted Q iteration, tailored for imperfect oligopolistic competition.

Findings

Effective bidding strategies learned from market data

Improved profit maximization in reactive power markets

Addresses limitations of active power market strategies

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.