Game Theory Approach for Energy Consumption Scheduling of a Community of Smart Grids

This study analyzes the potential of an independent method of dynamic pricing schemes to reduce the peak demand in the neighborhood area. For this, this work develops a collective energy consumption scheduling (CECS) algorithm in the residential sector based on a combination of energy consumption plans under a bi-level game theory method. The local level is responsible of gathering internal users' data and reducing the energy consumption in each residential building. The central level is the external demand management system that is focused on modeling a coalition between the local load management modules, as well as giving the redistributed demand profile to increase the global profit through a peak load minimization, financial gain and peak-to-average-ratio reduction. Four controllable appliances are included in load shifting and time activation cycling: clothes dryer units, heating, ventilation and air conditioning systems, electric water heater, and electric vehicle. The principle of the proposed CECS method relies on the flexibility of the user requirement that presents one of the contributions of this study. It proposes a novel framework for determining optimal non-static load management strategies, in which consumers can change their daily power demand patterns depending on their routines, preferences and requirements. Numerical results show that time-varying schemes encourage customers to condense their electricity consumption within low-price periods. However, by incorporating the proposed approach of coordinated scheduling algorithm significant profits in the whole and single level are demonstrated. Simulations infer that given the same load profiles, the proposed framework outperforms the non-coordinated strategy leading to important rates in total peak load minimization, total saving in electricity bills and reduction of peak-to-average-ratio.