A game theory based demand-side management in a smart microgrid considering price-responsive loads via a twofold sustainable energy justice portfolio

This paper introduces a demand-side integration (DSI) framework that upholds the efficacious electrical energy consumption to attain the aims of smart grid as well as the customers' requirement. The proposed DSI framework is based on a pre-paid orderly energy consumption strategy for a smart microgrid. The interaction between aggregators and end-use customers is captured as a Nash Bargaining Game. A dynamic electricity pricing scheme is implemented to derive the profitable daily electricity tariffs considering an elasticity-based model of price responsive load. The resources caused by proper response of small-scale consumers are integrated into day ahead energy scheduling problem. In order to cope the real-time deviations, the DSI program is accompanied by a supplementary pay-off module. The DSI framework is formulated as a stochastic optimization problem in the form of mixed integer nonlinear programming involving probabilistic representation of uncertainty in generation pattern of renewable energy resources. According to the simulation results, in contrast with the normal consumption paradigm, the load factor improves at least 1.36%, the net profit of unified entity enhances 0.27%, and the total gas emissions are mitigated about 10.9%. The outcomes demonstrate the accuracy and merit of the proposed method.

Automated summary

This paper introduces a demand-side integration (DSI) framework to achieve efficient electrical energy consumption for smart grids and meet customer requirements. The proposed framework is based on a prepaid orderly energy consumption strategy for smart microgrids and captures the interaction between aggregators and end-use customers as a Nash Bargaining Game. A dynamic electricity pricing scheme is implemented to determine profitable daily electricity tariffs, considering the price responsiveness of loads. The framework integrates resources from small-scale consumers to solve the day ahead energy scheduling problem. A supplementary pay-off module accompanies the DSI program to handle real-time deviations. The DSI framework is formulated as a stochastic optimization problem, considering probabilistic uncertainty in the generation pattern of renewable energy resources. Simulation results show improvements in load factor, net profit, and reduction in total gas emissions compared to the normal consumption paradigm, demonstrating the accuracy and merit of the proposed method.