Coordinated stochastic optimal energy management of grid-connected microgrids considering demand response, plug-in hybrid electric vehicles, and smart transformers

A microgrid comprises renewable and non-renewable power generating sources, controllable loads, energy storage devices and works as a single controllable entity. A gradual shift from conventional internal combustion engine-based vehicles to electric/hybrid electric vehicles has also led to a new load type in the power system. Moreover, demand-side management measures like demand response programs have become popular. This work deals with the optimal coordinated operation of a grid-connected AC microgrid consisting of controllable and uncontrollable power sources, battery storage units, considering plug-in hybrid electric vehicles and demand response programs. Stochastic models of renewable power sources, electric load demand, loads of hybrid electric vehicles (with battery charging characteristic), and grid power price are fed into ``Hong's 2 m point estimate method'' embedded optimal operating strategy. The objective is to minimize the cost of operation subject to the satisfaction of technical constraints. A nested stochastic optimization algorithm is implemented to find optimal generation schedule, battery dispatch strategy, and the best incentive value for an incentive-based demand response program. Different charging strategies of hybrid electric vehicles are studied, and their impacts on system operation are investigated. The optimal coordination between a voltage control scheme using a smart transformer with the energy management scheme is also investigated. Simulation studies on a thirty-three bus test system prove the efficacy of the proposed algorithm. The proposed coordinated optimal operating strategy reduces the operating cost by 17.53% similar to 17.74%. The system loss also reduces by 29.49% similar to 31.36%.

Automated summary

This study focuses on the optimal coordinated operation of a grid-connected AC microgrid consisting of controllable and uncontrollable power sources, battery storage units, considering plug-in hybrid electric vehicles and demand response programs. Through a nested stochastic optimization algorithm, a coordinated optimal operating strategy is proposed, which effectively reduces operating costs and system losses.