E-Vehicles are used for transportation and, with a vehicle-to-grid optimization approach, they may be used for supplying a backup source of energy for renewable energy sources. Renewable energy sources are integrated to maintain the demand of consumers, mitigate the active and reactive power losses, and maintain the voltage profile. Renewable energy sources are not supplied all day and, to meet the peak demand, extra electricity may be supplied through e-Vehicles. E-Vehicles with random integration may cause system unbalancing problems and need a solution. The objective of this paper is to integrate e-Vehicles with the grid as a backup source of energy through the grid-to-vehicle optimization approach by reducing active and reactive power losses and maintaining voltage profile. In this paper, three case studies are discussed: (i) integration of renewable energy sources alone; (ii) integration of e-Vehicles alone; (iii) integration of renewable energy sources and e-Vehicles in hybrid mode. The simulation results show the effectiveness of the integration and the active and reactive power losses are minimum when we used the third case.

A demand response algorithm for electricity trading using renewable energy sources using stackelberg game methodology. Energy consumption is rapidly increasing day by day and to maintain the balance between energy consumption and generation needs solution to generate electricity from renewable energy sources and demand response methodology is essential. Demand response (DR) algorithm maintain the balance and reduce the peak to average energy consumption ratio. Large number of constraints (i.e. number of appliance at consumer end, different consumers, renewable energy sources, electric vehicles, etc.) interface make the DR algorithm complex. Stackelberg game theory is provided the solution based on one leader N follower strategy where leader first decide their best response and followers select their best response on the basis of leaders strategy and then leader again optimize the new strategy. Process is repeat again and again to find nash equilibrium between consumers and utilities. Proposed methodology increase the generation from renewable energy sources and manage the appliances. Simulation results showed that stackelberg game with demand response algorithm is effective for optimal energy consumption control of appliances.

The game theory concept has been adapted for energy management between energy producers and consumers in the presence of renewable energy sources (RES) and electric vehicles (EVs). The objective of the paper is to increase the penetration of renewable energy sources and EVs by the Stackelberg game theory model with real-time pricing. RES are the energy generation sources located near load centers to manage the demand of consumers. The primary objective of EV is transportation, but for managing the energy demand, EV participate in grid operations as a back up source of energy. EV are charged during off-peak hours and discharged to the grid during peak hours. Consumers benefited in terms of higher payoffs to supply energy during peak hours. To increase the payoffs, consumers can shift their appliance operation from high price duration to low price duration and create a new peak energy consumption. To maintain the balance between consumers and utility, an optimization tool is essential. The Stackelberg game theory model is a one leader and N followers’ game, the leader first declares the energy price and the follower respond by selecting their best response for energy consumption. The leader can update its strategy again by considering the follower best response to obtain maximum benefits. The process to achieve the best response of the follower and leader is called the Stackelberg nash equilibrium. Objective of energy producers is to supply maximum energy through renewable energy sources and the use of EVs as storage units. The prime objective of consumers is to minimize energy tariff by scheduling the appliances.

In the present study the concept of non-cooperative game theory is proposed in the retail electricity market for introducing stepwise power tariff model (SPT) for agricultural consumers. The objective of the paper is to increase the energy generation through green energy generation sources (GEGS), introduction of plug-in hybrid electric vehicles, education of families, standard wiring and appliance efficiency in tariffs for agricultural consumers with non-cooperative game theory. Agricultural consumers are able to generate a huge amount of electricity through GEGS and are able to control the consumption in their own way, and the non-cooperative game theory is introduced. Energy consumption pattern varies with respect to time during off-peak load period to peak load period; during the peak load period the demand is high as compared to off peak load hour duration energy consumption for the consumers and policy makers interrupting the energy supply during peak hours for agricultural consumers. To maintain the balance between generation and consumption, energy saving is essentially required and needs to maintain the consumption patterns and increase the penetration level of distributed generation at the agricultural consumer end due to availability of land. This paper proposes an algorithm for a demand response methodology using SPT with non-cooperative game theory model based on monthly energy consumption to maintain the balance. The uncertainty about energy generation through GEGS taken in consideration using Monte-Carlo simulation (MCS). Simulation results obtained by the proposed methodology are compared with the conventional methodology of energy tariff used in India and provide better results for active consumers and generate a considerable amount of electricity through GEGS.

A stackelberg game algorithm is proposed to optimize real time demand response equilibrium between consumers and utilities with the integration of renewable energy sources. Stackelberg game is played between one leader and N followers to provide interaction between utility and consumers in the presence of energy management center. The objective of consumers is to minimize their energy tariffs by scheduling their appliances. Appliances are categorized as non-shiftable, shiftable and curtailable appliances based on their applications. The objective of the utility is to maximize its profit by managing peak to average energy consumption ratio, penetration of renewable energy sources as a primary source and during peak hours, integrate electric vehicles as a backup. The existence of a unique stackelberg equilibrium that provides an optimal energy consumption scheme to each appliance at the consumer end was demonstrated. The simulation results showed the optimal real time demand response using stackelberg game algorithm is effective for achieving the optimal load control.

Integration of distributed generators (DGs) using cooperative game theory (GT) at IEEE 33 bus system and 69 bus Ramchandrapura, Kota, India radial distribution feeder.