The concept of smart grid was introduced a decade ago. Demand side management (DSM) is one of the crucial aspects of smart grid that provides users with the opportunity to optimize their load usage pattern to fill the gap between energy supply and demand and reduce the peak to average ratio (PAR), thus resulting in energy and economic efficiency ultimately. The application of DSM programs is lucrative for both utility and consumers. Utilities can implement DSM programs to improve the system power quality, power reliability, system efficiency, and energy efficiency, while consumers can experience energy savings, reduction in peak demand, and improvement of system load profile, and they can also maximize usage of renewable energy resources (RERs). In this paper, some of the strategies of DSM including peak shaving and load scheduling are highlighted. Furthermore, the implementation of numerous optimization techniques on DSM is reviewed.

This work analyzes the sensitivity of 16 power system parameters that can be used in passive techniques to detect islanding and nonislanding events. It is observed that the rate of change of frequency over reactive power (df/dq) can effectively detect minute disturbances in power supply. It is also shown that active and reactive power mismatch has an opposing effect on the variation of frequency (df) in real time environment. As a result of this, a new passive technique based on df/dq is proposed. The simulation results indicate that the proposed technique is able to distinguish islanding from other events of similar signatures, such as load variation, capacitor switching, faults, and induction motor starting.

High energy consumption, rising environmental concerns and depleting fossil fuels demand an increase in clean energy production. The enhanced resiliency, efficiency and reliability offered by microgrids with distributed energy resources (DERs) have shown to be a promising alternative to the conventional grid system. Large-sized commercial customers like institutional complexes have put significant efforts to promote sustainability by establishing renewable energy systems at university campuses. This paper proposes the integration of a photovoltaic (PV) system, energy storage system (ESS) and electric vehicles (EV) at a University campus. An optimal energy management system (EMS) is proposed to optimally dispatch the energy from available energy resources. The problem is mapped in a Linear optimization problem and simulations are carried out in MATLAB. Simulation results showed that the proposed EMS ensures the continuous power supply and decreases the energy consumption cost by nearly 45%. The impact of EV as a storage tool is also observed. EVs acting as a source of energy reduced the energy cost by 45.58% and as a load by 19.33%. The impact on the cost for continuous power supply in case of a power outage is also analyzed.

Wind energy is one of the most common types of renewable energy resource. Due to its sustainability and environmental benefits, it is an emerging source for electric power generation. Rapid and random changes of wind speed makes it an irregular and inconsistent power source when connected to the grid, causing different technical problems in protection, power quality and generation dispatch control. Due to these problems, effective intermittent smoothing approaches for wind power output are crucially needed to minimize such problems. This paper reviews various intermittent smoothing approaches used in smoothing the output power fluctuations caused by wind energy. Problems associated with the inclusion of wind energy resources to grid are also briefly reviewed. From this review, it has been found that battery energy storage system is the most suitable and effective smoothing approach, provided that an effective control strategy is available for optimal utilization of battery energy system. This paper further demonstrates different control strategies built for battery energy storage system to obtain the smooth output wind power.