Matching Items (3)
Filtering by
- All Subjects: Battery
- Creators: Qin, Jiangchao
- Member of: ASU Electronic Theses and Dissertations
Description
Li-ion batteries are being used on a large scale varying from consumer electronics to electric vehicles. The key to efficient use of batteries is implementing a well-developed battery management system. Also, there is an opportunity for research for improving the battery performance in terms of size and capacity. For all this it is imperative to develop Li-ion cell model that replicate the performance of a physical cell unit. This report discusses a dual polarization cell model and a battery management system implemented to control the operation of the battery. The Li-ion cell is modelled, and the performance is observed in PLECS environment.
The main aspect of this report studies the viability of Li-ion battery application in Battery Energy Storage System (BESS) in Modular multilevel converter (MMC). MMC-based BESS is a promising solution for grid-level battery energy storage to accelerate utilization and integration of intermittent renewable energy resources, i.e., solar and wind energy. When the battery units are directly integrated in submodules (SMs) without dc-dc interfaced converters, this configuration provides highest system efficiency and lowest cost. However, the lifetime of battery will be affected by the low-frequency components contained in arm currents, which has not been thoroughly investigated. This paper investigates impact of various low-frequency arm-current ripples on lifetime of Li-ion battery cells and evaluate performance of battery charging and discharging in an MMC-BESS without dc-dc interfaced converters.
The main aspect of this report studies the viability of Li-ion battery application in Battery Energy Storage System (BESS) in Modular multilevel converter (MMC). MMC-based BESS is a promising solution for grid-level battery energy storage to accelerate utilization and integration of intermittent renewable energy resources, i.e., solar and wind energy. When the battery units are directly integrated in submodules (SMs) without dc-dc interfaced converters, this configuration provides highest system efficiency and lowest cost. However, the lifetime of battery will be affected by the low-frequency components contained in arm currents, which has not been thoroughly investigated. This paper investigates impact of various low-frequency arm-current ripples on lifetime of Li-ion battery cells and evaluate performance of battery charging and discharging in an MMC-BESS without dc-dc interfaced converters.
ContributorsPuranik, Ishaan (Author) / Qin, Jiangchao (Thesis advisor) / Karady, George G. (Committee member) / Yu, Hongbin (Committee member) / Arizona State University (Publisher)
Created2018
Description
Over the years, the growing penetration of renewable energy into the electricity market has resulted in a significant change in the electricity market price. This change makes the existing forecasting method prone to error, decreasing the economic benefits. Hence, more precise forecasting methods need to be developed. This paper starts with a survey and benchmark of existing machine learning approaches for forecasting the real-time market (RTM) price. While these methods provide sufficient modeling capability via supervised learning, their accuracy is still limited due to the single data source, e.g., historical price information only. In this paper, a novel two-stage supervised learning approach is proposed by diversifying the data sources such as highly correlated power data. This idea is inspired by the recent load forecasting methods that have shown extremely well performances. Specifically, the proposed two-stage method, namely the rerouted method, learns two types of mapping rules. The first one is the mapping between the historical wind power and the historical price. The second is the forecasting rule for wind generation. Based on the two rules, we forecast the price via the forecasted generation and the first learned mapping between power and price. Additionally, we observed that it is not the more training data the better, leading to our validation steps to quantify the best training intervals for different datasets. We conduct comparisons of numerical results between existing methods and the proposed methods based on datasets from the Electric Reliability Council of Texas (ERCOT). For each machine learning step, we examine different learning methods, such as polynomial regression, support vector regression, neural network, and deep neural network. The results show that the proposed method is significantly better than existing approaches when renewables are involved.
ContributorsLuo, Shuman (Author) / Weng, Yang (Thesis advisor) / Lei, Qin (Committee member) / Qin, Jiangchao (Committee member) / Arizona State University (Publisher)
Created2019
Description
Due to the increasing trend of electricity price for the future and the price reduction of solar electronics price led by the policy stimulus and the technological improvement, the residential distribution solar photovoltaic (PV) system’s market is prosperous. Excess energy can be sold back to the grid, however peak demand of a residential customer typically occurs in late afternoon/early evening when PV systems are not a productive. The solar PV system can provide residential customers sufficient energy during the daytime, even the exceeding energy can be sold back to the grid especially during the day with good sunlight, however, the peak demand of a regular family always appears during late afternoon and early evening which are not productive time for PV system. In this case, the PV customers only need the grid energy when other customers also need it the most. Because of the lower contribution of PV systems during times of peak demand, utilities are beginning to adjust rate structures to better align the bills paid by PV customers with the cost to the utility to serve those customers. Different rate structures include higher fixed charges, higher on-peak electricity prices, on-peak demand charges, or prices based on avoided costs. The demand charge and the on-peak energy charge significantly reduced the savings brought by the PV system. This will result in a longer the customer’s payback period. Eventually PV customers are not saving a lot in their electricity bill compare to those customers who do not own a PV system.
A battery system is a promising technology that can improve monthly bill savings since a battery can store the solar energy and the off-peak grid energy and release it later during the on-peak hours. Sponsored by Salt River Project (SRP), a smart home model consists 1.35 kW PV panels, a 7.76 kWh lithium-ion battery and an adjustable resistive load bank was built on the roof of Engineering Research Center (ERC) building. For analysis, data was scaled up by 6/1.35 times to simulate a real residential PV setup. The testing data had been continuously recorded for more than one year (Aug.2014 - Oct.2015) and a battery charging strategy was developed based on those data. The work of this thesis deals with the idea of this charging strategy and the economic benefits this charging strategy can bring to the PV customers. Part of this research work has been wrote into a conference paper which is accepted by IEEE PES General Meeting 2016. A new and larger system has been installed on the roof with 6 kW PV modules and 6 kW output integrated electronics. This project will go on and the method come up by this thesis will be tested.
A battery system is a promising technology that can improve monthly bill savings since a battery can store the solar energy and the off-peak grid energy and release it later during the on-peak hours. Sponsored by Salt River Project (SRP), a smart home model consists 1.35 kW PV panels, a 7.76 kWh lithium-ion battery and an adjustable resistive load bank was built on the roof of Engineering Research Center (ERC) building. For analysis, data was scaled up by 6/1.35 times to simulate a real residential PV setup. The testing data had been continuously recorded for more than one year (Aug.2014 - Oct.2015) and a battery charging strategy was developed based on those data. The work of this thesis deals with the idea of this charging strategy and the economic benefits this charging strategy can bring to the PV customers. Part of this research work has been wrote into a conference paper which is accepted by IEEE PES General Meeting 2016. A new and larger system has been installed on the roof with 6 kW PV modules and 6 kW output integrated electronics. This project will go on and the method come up by this thesis will be tested.
ContributorsWang, Xin'an (Author) / Karady, George G. (Thesis advisor) / Smedley, Grant (Committee member) / Qin, Jiangchao (Committee member) / Ayyanar, Raja (Committee member) / Arizona State University (Publisher)
Created2016