Optimizing Demand Response in Deregulated Electricity Markets: A Customer-Centric Game Theory Approach
Date
2023-08-01
Authors
Goudarzi, Arman
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Abstract
In the era of IoT-enabled smart grid technologies and the ever-increasing integration of renewable energy sources, the need for efficient and customer-oriented demand response programs is becoming crucial for the stability and flexibility of power systems. In this regard, this report presents an innovative customercentric game theory-based demand response (CC-GTDR) for managing electricity consumption during periods of high demand in a deregulated electricity market. The proposed CC-GTDR method exceptionally combines both incentive and price-based demand response programs while emphasizing customer benefits and flexibility of choice. A fuzzy analytic hierarchy process based on non-linear programming (FAHPNLP) is employed to determine the optimum weightings of the designed multi-criteria objective function of the study. To solve the proposed model, a hybrid optimization algorithm is implemented, which merges enthusiasm-assisted teaching and learning-based optimization (EaTLBO) with an enhanced variant of particle swarm optimization (EPSO). The study investigates various dynamic pricing mechanisms, such as time-of-use pricing, real-time pricing, and their combinations, in deregulated electricity markets. The proposed approach demonstrates significant improvements in overall load and peak load reductions, as well as utility profit gains. Additionally, the integration of renewable energy sources (RESs) within the CCGTDR and profit-based dynamic cost environmental economic dispatch (DCEED) model results in substantial reductions in NOx emissions. The developed CC-GTDR model contributes to a more resilient and efficient electrical system by prioritizing customer engagement and empowerment, ultimately enhancing grid reliability and facilitating the integration of renewable resources.
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Keywords
Customer-centric DRP, Dynamic pricing mechanisms, Deregulated electricity market, dynamically combined environmental economic dispatch (DCEED), Hybrid optimization algorithm