A game theory-based interactive demand response for handling dynamic prices in security-constrained electricity markets

Abstract

The advent of smart grid technologies due to the explosive increase in the electricity demand, has necessitated utilities around the globe in establishing intelligent demand response programs (DRPs) to influence customers' consumption patterns. The successful implementation of DRPs together with dynamic pricing strategies not only reduces energy prices in electricity markets but also improves network reliability and overall system efficiency. This study proposes a game theory-based DRP (GTDRP) which merges the incentive- and price-based DRP concepts with a focus on residential, commercial, and industrial sectors. Three pricing strategies are structured and compared, that is, fixed pricing, time-of-use pricing (for both utility- and customer-side), and real-time pricing along with their combination. Also, an enthusiasm-aided teaching and the learning-based optimization algorithm are developed to solve the GTDRP model through a self-adaptive power violation criterion. To validate the practicality of the presented model, three case studies through seven different scenarios are investigated. Results of the case studies demonstrate that the formulated multi-criteria security-constrained GTDRP can create a win-win situation for the utility and customers in the electricity markets, such that the utility profits increase, the customers' related costs reduce, and the load curve is flattened.