ABSTRACT
Several countries around the world have restructured their power sectors and introduced separate energy and ancillary services markets for improving efficiency. Generally, energy market is a day-ahead market, and ancillary services market is near to real-time market. However, due to various uncertainties like load forecasting error, transmission congestion, transmission losses, rivals’ bidding behaviour, etc., generators participating in the electricity markets are subjected to physical and financial risks. This flawed nature of the electricity markets has forced the participants to adopt a new way of understanding their participation in electricity markets. In the competitive environment, the suppliers’ revenue depends on their ability to sell the energy, and buyers’ saving depends on their active participation in the Electricity Market (EM). Matching of supply and demand is continuously required in power systems to ensure their stable operation. However, during the actual time of delivery, imbalance between supply and demand is experienced due to frequent change in load. This imbalance may disturb the system frequency. Market operators try to maintain this balance by adopting energy-balancing services. These services are traded in both the day-ahead and the real-time balancing market. A supplier estimates the market clearing price, rivals’ bidding strategies and other contingencies to enhance their profit and reduce the risk of loss of chance. Due to technical and regulatory constraints, various bidding models, forecasted demand and rivals’ bid strategies make bidding strategy problem as a stochastic optimization problem. Application of heuristic methods to the strategic bidding problem has been found to be the best to deal with the stochastic nature of this problem. This is because heuristic techniques are less affected by the size and non-linearity of the problem and can converge to the optimal solution, where most of the analytical methods fail to converge. Hence, this chapter covers optimal decision making of a generating company under uncertainty and presents a heuristic approach-based artificial bee colony algorithm to develop optimal strategy of a generator. Further, a case study has been given to show the impact of coordinated bidding strategy on the profit of generating company under uncertainty.
