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In coming years, the widespread use of Plug-in Hybrid Electric Vehicles (PHEVs) will impose a significant burden on the existing electric grid. The situation may worsen due to uncontrolled charging strategies adopted for PHEVs. On the other hand, these PHEVs, if charged through proper control mechanisms may reduce additional dynamic load demands. Also, if utilized properly, they may provide significant support to electric grid from time to time. The entire process of regulating the power exchanged with PHEVs w.r.t the existing grid conditions is well known as Demand Side Management (DSM). To indulge PHEVs in DSM, an accurate estimate of characteristics of PHEVs, both on-road and off-road, is necessary. Thus, this study aims to mathematically model the behaviour of four imperative parameters of PHEVs. These are dynamic travel behaviour, battery state-of-charge (SOC) requirements, the energy demands of PHEVs and, total power exchanged by PHEVs with the electric grid. In addition to this, a smart charging strategy is proposed and tested to verify the ability of PHEVs for participating in DSM for peak load management. The impacts of uncontrolled charging and smart charging of PHEVs on grid power demands are also discussed.

Distributed Generation has drawn the attention of industrialists and researchers for quite a time now due to the advantages it brings loads. In addition to cost-effective and environmentally friendly, but also brings higher reliability coefficient power system. The DG unit is placed close to the load, rather than increasing the capacity of main generator. This methodology brings many benefits, but has to address some of the challenges. The main is to find the optimal location and size of DG units between them. The purpose of this paper is distributed generation by adding an additional means to reduce losses on the line. This paper attempts to optimize the technology to solve the problem of optimal location and size through the development of multi-objective particle swarm. The problem has been reduced to a mathematical optimization problem by developing a fitness function considering losses and voltage distribution line. Fitness function by using the optimal value of the size and location of this algorithm was found to be minimized. IEEE-14 bus system is being considered, in order to test the proposed algorithm and the results show improved performance in terms of accuracy and convergence rate.

Summary: This article proposes a combined scheme to reduce the substation demand with conservation voltage reduction (CVR) alongside intelligent photovoltaic (PV) inverter control functions. CVR maintains the power delivered to the customers in lower service voltage range (120‐114)V by not influencing electric devices' performance at the consumer premises. Since most of the loads are voltage‐dependent at the consumer side, proper voltage control can obtain substantial demand reduction and energy savings. In the proposed work, CVR is applied to both medium‐sized (IEEE‐123 node test feeder) and large‐sized (IEEE‐8500 node test feeder) unbalanced distribution feeders without PV and with intelligent PV inverter. In addition, the transient nature of PV output power is also examined during the movement of the clouds. The smart PV inverter utilizes various control functions (Volt‐VAr, Volt‐VAr [hysteresis], Volt‐Watt, and combi mode) to control the system's active and reactive power. Results reveal that operating CVR with intelligent PV inverter in the hysteresis Volt‐VAr mode helps achieve higher substation demand reduction than with only CVR operation. This combination reduces system losses and contributes to energy savings when better voltage regulation is provided. It also helps in maintaining the reliability and efficiency of the distribution network. [ABSTRACT FROM AUTHOR]