{'fraction_non_alphanumeric': 0.020930232558139535, 'fraction_numerical': 0.0, 'mean_word_length': 5.4874371859296485, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Information gap decision theory as a tool for strategic bidding in competitive electricity markets

Market based contracting introduces increased competition in the power industry, and creates a need for optimized bids and bidding strategies. To maximize the expected monetary value (EMV) of a bid, generation companies (GENCOs) must strive to use models better than their competitors. Such models should account for factors such as buyers, market power, market mechanisms, other competitors, substitutes, and equipment status. This paper explores bounds on the probability distribution describing the competitors' bids. This weak probabilistic information is used to formulate a basic competitive bidding problem. In this environment, the bidder is expected to perform better provided they are informed about factors impacting the competitor's bids. However, the acquisition of this kind of information involves costs that may exceed the expected benefit. Therefore, the bidder must decide whether or not to acquire information to alter the optimal bid. This paper explores use of information gap decision theory to quantify severe uncertainty. The value of additional information is compared under a more informative info-gap model where it determines the demand value of the information.