APPLICATION OF ENERGY STORAGE AND DEMAND RESPONSE FOR PEAK SHAVING/LOAD LEVELING IN A SMART DISTRIBUTION SYSTEM

The current advances toward reduction of CO2 emission, mitigation of innate characteristic of renewable energy sources, balancing generation and consumption, including an increase in demand for high power quality and reliability necessitates the use of energy storage and demand response. The potential for using energy storage and demand response promise to have a significant impact on schemes for voltage control in a smart distribution system. This paper presents the application of energy storage and demand response in peak shaving/load levelling. An integrated framework on hybrid Particle Swarm Optimization/Gravitational Search Algorithm (PSOGSA) is used for VAr control devices, energy storage and demand response optimization schedule. An extensive multiperiod modelling approach developed, used quasi-time sequence analysis, based on Open distributed system simulator (OpenDSS) with Matlab interface to capture the intermittency of the wind and solar PV energy output in a chronological 24-hour basis. The result confirms that the use of energy storage and demand response effectively assists to harness intermittent renewable energy resources and provide additional flexibility to the system to hedge against the fluctuations of variable renewable energy sources output.