Strategic bidding in an energy brokerage

The main contribution of this research is the definition, and the demonstration of use, of a framework for the development and evaluation of bidding strategies, for participants to use, in preparing and submitting bids to an energy brokerage market. The framework includes the rules under which the market operates, the different types of participants and their objectives, the factors that affect the bidding of the participants, strategies that consider these factors and achieve the objectives, and a simulator to simulate market conditions, including competition from other participants, with which to test these strategies;Strategies that attempt to include competitor behavior by using available market information are developed. A lower bound on the profit from bidding is derived, which is useful in providing an objective function that can be optimized using the limited information assumed to be available in this research. This is followed by derivations for optimal bids that maximize this lower bound, for different assumptions about the probability distribution of the competitors;The simulator is expected to be helpful in testing of the strategies. However, the strategies will be independent of the simulator, and will be applicable to participants who choose a different (presumably more advanced) tool for evaluation. The contribution of this research includes original ways to utilize the information generated by the simulator;Some of the results of the simulations performed using this simulator to test the strategies developed are presented and analyzed. Also, based on these results, some heuristics were developed to improve the performance of the strategies. Results from implementing these heuristics are also presented;A qualitative treatment of the scheduling factors that might affect bidding strategies is presented, followed by numerical examples to illustrate the effects. A treatment of risk preferences by using results from recent developments in utility theory and risk preference functions by researchers in economics, is presented. This is followed by the modeling of bidding objectives as expected utility maximizations, and the comparison of results from using this type of objective to using the expected profit maximization objective for various scheduling scenarios