Electrification of the transportation sector can play a vital role in reshaping smart cities. With an increasing number of electric vehicles (EVs) on the road, deployment of well-planned and efficient charging infrastructure is highly desirable. Unlike level 1 and level 2 charging stations, level 3 chargers are super-fast in charging EVs. However, their installation at every possible site is not techno-economically justifiable because level 3 chargers may cause violation of critical system parameters due to their high power consumption. In this paper, we demonstrate an optimized combination of all three types of EV chargers for efficiently managing the EV load while minimizing installation cost, losses, and distribution transformer loading. Effects of photovoltaic (PV) generation are also incorporated in the analysis. Due to the uncertain nature of vehicle users, EV load is modeled as a stochastic process. Particle swarm optimization (PSO) is used to solve the constrained nonlinear stochastic problem. MATLAB and OpenDSS are used to simulate the model. The proposed idea is validated on the real distribution system of the National University of Sciences and Technology (NUST) Pakistan. Results show that an optimized combination of chargers placed at judicious locations can greatly reduce cost from $3.55 million to $1.99 million, daily losses from 787kWh to 286kWh and distribution transformer congestion from 58% to 22% when compared to scenario of optimized placement of level 3 chargers for 20% penetration level in commercial feeders. In residential feeder, these statistics are improved from $2.52 to $0.81 million, from 2167kWh to 398kWh and from 106% to 14%, respectively. It is also realized that the integration of PV improves voltage profile and reduces the negative impact of EV load. Our optimization model can work for commercial areas such as offices, university campuses, and industries as well as residential colonies. INDEX TERMS Charging stations placement, distribution system, electric vehicles (EVs), optimization. NOMENCLATURE SETS N Set of buses in the system T Set of time periods M Set of line sections O Set of types of chargers E Set of electric vehicles INDICES i Index of bus number t Index of time period j Index of line section l Index of level of charging station e Index of electric vehicle The associate editor coordinating the review of this manuscript and approving it for publication was Zhiyi Li. PARAMETERS P j,loss Power loss of jth line section C Charging power of a charger SOC init Initial state of charge of a battery c Cost of a charger c p,l Per unit electrical energy cost S j,max Maximum transfer capacity of line section j η ch Charging efficiency of EV d max Maximum range when EV is fully charged VARIABLES n Number of charging station V i,t Voltage magnitude of bus i at time interval t S j,t Power flow through line section j at time interval t dist trav,e Travelled distance by electric vehicle e
Abstract-The primary objective of the conventional optimal phasor measurement unit (PMU) placement problem is the minimization of the number of PMU devices that, when placed in a power system, measure all bus voltages. However, due to advancements in the field of relay technology, digital relays can now act as PMUs. This has significantly reduced device costs. Moreover, although the goal is to observe all the buses, the devices themselves can only be placed in substations, whose upgrade costs are much higher than those of the devices. Considering these factors, the approach proposed here simultaneously optimizes the number of substations where traditional PMUs and dual-use line relay PMUs can be placed. The general optimal substation coverage (GOSC) algorithm presented in this paper is also able to incorporate practical requirements such as redundancy in the measurement of critical elements of the system, and estimation of the tap ratios of the transformers present. Simulation results indicate that the GOSC algorithm provides significant techno-economic benefits.
A phasor measurement unit (PMU) only state estimator is intrinsically superior to its SCADA analogue with respect to speed, performance, and reliability. However, ensuring the quality of the data stream which enters the linear estimator is crucial before establishing it as the front end of an EMS or other network applications. One approach is to pre-process the phasor data before it arrives at the linear estimator. This paper presents an algorithm for synchrophasor data conditioning and repair that fits neatly as a prefix into the existing linear state estimation formulation. The methodology has been tested using field data obtained from PMUs installed in Dominion Virginia Power's (DVP's) EHV network. The results indicate that the proposed technique provides a computationally simple, elegant solution to the synchrophasor data quality problem.Index Terms-Data quality, Kalman filter, phasor measurement unit (PMU), state estimation.
The primary objective of the conventional optimal phasor measurement unit (PMU) placement problem is the minimization of the number of PMU devices that, when placed in a power system, measures all bus voltages. However, due to advancements made in the field of relay technology, digital relays can now act as PMUs. This has significantly reduced device costs. Moreover, although the goal is to observe all the buses, the devices themselves can only be placed in substations, whose upgrade costs are much higher than those of the devices. Considering these factors, the approach proposed here optimizes the number of substations where traditional bus type PMUs, as well as the more modern dualuse line relay PMUs, can be placed. Called the general optimal substation coverage (GOSC) algorithm, it is also able to incorporate practical challenges such as redundancy in measurement of critical elements of the system, and estimate tap ratios of the transformers present. The results indicate that the proposed GOSC algorithm has significant techno-economic benefits.
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