A new approach to demand response in a microgrid based on coordination control between smart meter and distributed superconducting magnetic energy storage unit. (December 2020) - Kouache, I.; Sebaa, M.; Bey, M.; Allaoui, T.; Denai, M.
Highlights: DSMES as an improved device is a promising technology for improving power quality and lower price consumption in Smart Grids. An improved management system based on Smart Meter as the intelligent manager of power plan distribution. A control strategy allows to the DSMES unit to exchange the power with the Micro grid and intervene in the level of grid for enhance the power quality for power systems. A competitive algorithm used for optimizing Cost of energy consumption, and allowed to consumer choose your consumption in lower price time. Abstract: Energy storage and demand response are becoming an increasingly valuable solution for the enhancement of stability and reliability of the electricity grid with high penetration of distributed energy sources such as wind and solar. This paper proposes an intelligent energy management scheme to allow different distributed generation sources and energy storage technologies to actively participate in demand response via the deployed smart meter infrastructure. A Distributed Superconducting Magnetic Energy Storage (D-SMES) device is integrated into the network to deliver instantaneous and large bursts of power to support the grid under short-term disturbances. The proposed demand response-based energy management algorithm receives, via the smart meter infrastructure, the information from the electricity network such as the energy cost, upper load adjustment limit, and the State-of-Charge (SOC) of the storage system then, under very specific conditions, a decision is formulated as a control signal for charging or discharging the storage system, aiming at minimizing the total energy while avoiding any load shedding. The effectiveness of the proposed demand response approach for controlling generation, storage and demand coordination and energy cost reduction is demonstrated on a sample power system simulated in MATLAB/SimPowerSystems toolbox and evaluated under different case study scenarios.