Neural Fitted Q Iteration based Optimal Bidding Strategy in Real Time Reactive Power Market_1
This work is significant for generation companies operating in reactive power markets, as it provides a method for learning optimal bidding strategies in a previously unexplored and complex market environment.
This paper addresses the challenge of optimal bidding strategies in real-time reactive power markets, a domain where existing active power market strategies are unsuitable due to unique market dynamics and network voltage impacts. The authors propose a novel approach to learn optimal bidding strategies directly from market observations and experience within a three-stage reactive power market.
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.