Isha Sharma

Modeling and impacts of smart charging PEVs in residential distribution systems

This paper presents a new modeling framework for inclusion of the charging operations of Plug-in Electric Vehicles (PEVs) within a three-phase unbalanced, residential, distribution system. Coordinated charging of the PEVs is proposed to minimize the total energy drawn from the substation, total losses in the system and the total cost of charging the PEVs. Detailed studies examine the impact of PEVs on the overall system load profile, bus load profiles, feeder currents, voltages, taps and capacitor switching. The proposed model can be used to maximize PEV charging over a 24 hour time-frame or the utilization of feeder capacity to charge the PEVs. A practical distribution test feeder is presented to demonstrate the features of the proposed model.

Smart operation of unbalanced distribution systems with PVs and Energy Storage

This paper presents a novel framework for optimal operation of Energy Storage Systems (ESSs) in coordination with Renewable Energy Resources (RERs) in a distribution system. The proposed model is a multi-period, three-phase, unbalanced Distribution Optimal Power Flow (DOPF) wherein mathematical models for solar photovoltaic (PV) generation and ESS are integrated. Two objectives are considered from the perspective of a Local Distribution Company (LDC): one objective seeks to minimize the LDCs energy procurement costs using a time-of-use rate, and the second objective minimizes total losses of the feeder. Multiple case studies are carried out to examine the operational scheduling of the ESS when placed nearby the substation and at the end of the feeder. Relevant simulation results are presented and discussed to highlight the effectiveness of the proposed method for optimal operation of distribution systems with ESS and RERs. Furthermore, the effect of increasing PV penetration on the operating schedule of the ESS is also examined.

Analysis of unbalanced distribution systems with solar PV penetration

This paper presents the modeling and analysis of solar photovoltaic (PV) generation systems penetrating into low voltage distribution feeders. The solar PV module is modeled as a constant PQ generation source and incorporated within an unbalanced distribution feeder. Furthermore, a distribution optimal power flow (DOPF) model is developed that includes the PV module and examines feeder operation from the perspective of the Local Distribution Company (LDC). Objective functions such as minimization of total energy drawn from the substation and minimization of total losses are considered. A realistic feeder system of Hydro One Inc. is used for the studies for various penetration levels of PV resources. Results show that total losses and total energy drawn from the substation is reduced with increased penetration of solar PV resources. However, this is accompanied with an increase in reactive power drawn from the external grid.

Optimal sizing of battery energy storage systems in unbalanced distribution feeders

This paper presents a mathematical model for optimal sizing of battery energy storage systems (BESS) in an unbalanced distribution system. The vanadium radox (VR) and sodium sulfur (NaS) based batteries are considered in this study. Detailed life-cycle cost analysis is carried out to first identify the long-term cost of the batteries, which is then included in the proposed model. Two case studies are carried out: first the effect of the local distribution company (LDC) savings on the battery size is examined, and second the optimal size of the battery is determined by solving a comprehensive optimization problem. The proposed model seeks to maximize the savings of the LDC accrued from BESS operation, i.e., maximize the difference of discharging and charging cost of the BESS, while the investment cost is minimized. The IEEE 13-node test feeder is used to test the proposed model.

Residential micro-hub load model using neural network

This paper presents the modeling of a residential micro-hub load based on real measurements and simulation data obtained using the Energy Hub Management System (EHMS) model of a residential load. A neural network (NN) is used to estimate the load model as a function of time, temperature, peak demand, and energy price. Different NN training approaches are compared to determine the best function to be used, based on the available data. Also, the number of hidden layer neurons are varied to obtain the best fit for the NN model. The results show that the proposed NN model is able to properly represent the behavior of an actual residential micro-hub.

Voltage impact analyses of solar photovoltaics on distribution load tap changer operations

Recently, the penetration of solar photovoltaic (PV) systems in Low Voltage (LV) distribution feeders has significantly increased and is expected to increase further in the coming years. Variability in solar PV output and high R/X ratio of LV distribution feeder results in voltage fluctuations. Local distribution companies (LDCs) rely on load tap changing transformers or feeder voltage regulators to mitigate voltage fluctuations. The objective of this paper is to examine the operational impact of increase in solar PV penetration on LV distribution networks through quasi-static time series analysis. IEEE 13-node test feeder is used in this paper as a test system. Simulations are carried out for a one-year period with 1-minute time step using OpenDSS. Results show significant increase in the total number of tap operations as solar PV penetration increases. Impact on nodal voltages and feeder net power is also discussed in detail. Finally, this paper suggests various mitigation strategies that can be used by LDCs to overcome this issue.

Statistical analysis of solar PV power frequency spectrum for optimal employment of building loads

In this paper, a statistical analysis of the frequency spectrum of solar photovoltaic (PV) power output is conducted. This analysis quantifies the frequency content that can be used for purposes such as developing optimal employment of building loads and distributed energy resources. One year of solar PV power output data was collected and analyzed using one-second resolution to find ideal bounds and levels for the different frequency components. The annual, seasonal, and monthly statistics of the PV frequency content are computed and illustrated in boxplot format. To examine the compatibility of building loads for PV consumption, a spectral analysis of building loads such as Heating, Ventilation and AirConditioning (HVAC) units and water heaters was performed. This defined the bandwidth over which these devices can operate. Results show that nearly all of the PV output (about 98%) is contained within frequencies lower than 1 mHz (equivalent to ∼15 min), which is compatible for consumption with local building loads such as HVAC units and water heaters. Medium frequencies in the range of ∼15 min to ∼1 min are likely to be suitable for consumption by fan equipment of variable air volume HVAC systems that have time constants in the range of few seconds to few minutes. This study indicates that most of the PV generation can be consumed by building loads with the help of proper control strategies, thereby reducing impact on the grid and the size of storage systems.