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