Interactive game based peak shaving management in the AC/DC hybrid distribution network

Abstract

The large-scale integration of DC loads such as electric vehicles and air conditioners has increased the peak demand of the distribution network. Therefore, this paper proposed a load peak shaving operation management model based on interactive games among multi-stakeholder (e.g., distribution network operator, energy storage owner and electric vehicle users) in AC/DC hybrid distribution network. The supply and demand balance among multiple AC networks can be realized through DC network with the help of electric vehicles and energy storage when the distribution network operator adjusts the time-of-use electricity price in the peak shaving management model. Distribution network operators aim at minimizing the peak-to-valley difference and maximizing the operation revenue while energy storage owner and electric vehicle users seek for the greatest benefits in interactive game process. The above game model is constructed in a master-slave framework, where the existence and uniqueness of the game equilibrium is also proved. Finally, the numerical results show that the proposed operation management model can effectively reduce the peak-to-valley difference when ensuring the interests of all stakeholders.

Dynamic pricing strategy for efficient electric vehicle charging and discharging in microgrids using multi-objective jaya algorithm

“…EVs have been efficiently discharged at an average power rate in accordance with the actual load in 24-hour schedule. The cost optimization strategy using Jaya algorithm for EV charging/discharging has resulted in an average reduction of 23.12% w.r.t PSO-GA [43], 15.18% w.r.t PLM-ESA [44,45] and 9.74% w.r.t GWO [19] described in figure 9. The MOjaya algorithm aligns the EVs SOCs as per customer expectations by providing an optimal solution that caters to customer interests.…”

“…Through this iterative process, the algorithm converges towards charging/discharging schedules that achieve the desired balance between cost minimization and power constraints. The MOJaya algorithm utilizes less energy i.e., 13.33% as compared to GWO [43], 16.66% w.r.t PLM-ESA [44,45] and 26.66% w.r.t PSO-GA [19]. Thus, the algorithm provides better efficiency by reducing the overall power consumption and costs as compared to other algorithms like GWO, PSO-GA and PLM-ESA as described in figure 14.…”