Full-mode control in utilizing stored energy in lithium-ion batteries based on forecasted PV output implemented for HEMS
Renewable energy resources such as photovoltaic (PV) and wind energy are crucial to counter an incoming energy crisis in the near future. Nevertheless, an intermittent behavior of input energy that is generated through PV panels requires a proper battery energy storage system (BESS) in order to alle...
Saved in:
| Main Author: | |
|---|---|
| Format: | Thesis |
| Language: | English |
| Published: |
2016
|
| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/16953/19/Full-mode%20control%20in%20utilizing%20stored%20energy%20in%20lithium-ion%20batteries%20based%20on%20forecasted%20PV%20output%20implemented%20for%20HEMS.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Renewable energy resources such as photovoltaic (PV) and wind energy are crucial to counter an incoming energy crisis in the near future. Nevertheless, an intermittent behavior of input energy that is generated through PV panels requires a proper battery energy storage system (BESS) in order to alleviate', its output for the sake of the load. Moreover, when PV generators are integrated with storage batteries, a constructive mechanism needs to be well structured in order to securely control the flow of energy in the batteries during the charging/discharging process so that the risk of overcharge/ over-discharge of the batteries can be significantly prevented. Furthermore, it is extremely essential to implement a control method that is capable to fully utilize a stored energy in the scope of small-scale BESS to the load regularly in the first place before it is further scaled up to a farm-scale or mega-structure. In this study, an energy control scheme that considers and executes a next-day forecast of generation as an input data has been proposed. Originally, numerical weather predictions of solar radiation are performed based on Grid Point Value (GPV) using relative humidity, precipitation and cloud cover parameterization. Main approach is to test how sensitive the proposed scheme works with the entire system, experimentally and how it deals with errors that caused by the forecast data. Thus, the charging (generation) and discharging (consumption) processes of the batteries were performed separately during the day and night, respectively. The amount of energy consumption determined by this control is the necessary amount of energy to fully charge the batteries on the next day based on the GPV-forecast data and the maximum storage size of the batteries used in here is 30 Ah. Basically, experimental equipment was structured to form a stable 100 V DC power supply for the load and the system's operation was completely administered by an RX621 microcontroller. As a result, the forecasting errors, if any, on the days when generation was less than 10 Ah or more than 30 Ah, were negligible since 10 Ah or 30 111 Ah of energy were supplied from the batteries to the load consistently during rainy or sunny days, respectively. Impressively, average energy consumption for January to June 2015 is considerably high with approximately 20.7 Ah, respectively, which suggests that the proposed control succeeded in utilizing energy corresponded to over 95.1% of the average C for 2011-2014. Thus, it is desirable if the entire proposed system might become a trigger for other researchers to structure more comprehensive EMS applications that are more reliable, efficient and sophisticated in the future. |
|---|