Fuzzy set based decision support system for transactions of electricity in a deregulated environment

• Main Author: Dhaliwal, Maninder Kaur
• Language: English
• Published: 2009
• Online Access: http://hdl.handle.net/2429/12059

In the restructured electricity market environment, the market participants conduct their power transactions with an aim to maximize their profits. Electricity is typically traded either through an auction at an open-access power exchange market or directly between supplier and retailer/consumer through bilateral and/or multilateral contracts. In the power exchange, the balance of supply and demand determines the spot market price. These transactions are purely economic and are subject to the physical constraints of the transmission system. The transmission grid is controlled by an Independent System Operator (ISO), and information about the system operation is restricted and rarely available to the market entrants. The market players generally receive partial information about the system conditions, such as a forecast of the total demand. Hence, the pivotal information in conducting the spot market transactions of electricity is price and demand. In some cases, a direct bilateral contract between suppliers and consumers can provide an attractive alternative to the spot market pricing, where the price can be volatile due to strategic behavior of market participants or tight demand-supply balance. The most likely measure of suitability of a bilateral contract is its comparison with the market price. However, the spot market price tends to be significantly volatile. Therefore, suitable methods for representing the volatile market price are needed. The traditional modeling methods are primarily based on statistical and probabilistic approaches, and it may not be entirely suitable to apply these stochastic methods to model the data generated by human activities such as power exchange markets. Besides, most of the existing models are aimed at stock exchanges, and may not necessarily be applicable to the electricity markets. In this research work, a data representation model based on extended fuzzy regression is developed. The model represents the highly volatile demand-price relations as a range and estimates the possible distribution of the price range for a given value of a demand forecast. The highlight of the model is its capability to preserve the aggregate information contained in the original data set such as the uncertainty. The model was tested using actual data from California Power Exchange and results were found to be promising. Based on the proposed model, a procedure for evaluation of bilateral contracts in an open market environment is developed.