TY - GEN
T1 - Rural Indian microgrid design optimization - Intelligent battery sizing
AU - Rajan, Gandhi
AU - Kavakuntala, Meghana
AU - Rajkumar, Vetrivel Subramaniam
AU - Gnanavel, Sivaroophini
AU - Vijayaraghavan, Vineeth
PY - 2017/12/22
Y1 - 2017/12/22
N2 - This paper proposes an enhanced three step design framework for microgrids designed with fixed capacity shortages (CS) that optimizes cost using load prioritization, battery thresholds and predicted solar data in the design phase instead of operational phase to reduce capital expenditure. This is achieved through reduction in battery size. The framework uses HOMER to model microgrids that are cost optimized for a given CS and installation location. As the first step of the redesign, load prioritization is used to resize the system into a new configuration, termed as Default System Size (DSS). The battery size in DSS is subsequently optimized over second and third stages of design. The second stage, Battery Threshold Management (BTM) uses efficiencies brought about by setting battery thresholds using load information for a 24-hour autonomy. In the third step - Prediction Management (PM), the operational efficiency brought about by solar generation prediction data is incorporated into the design framework. The proposed framework is validated using 6 individual microgrids set in different rural Indian locations.
AB - This paper proposes an enhanced three step design framework for microgrids designed with fixed capacity shortages (CS) that optimizes cost using load prioritization, battery thresholds and predicted solar data in the design phase instead of operational phase to reduce capital expenditure. This is achieved through reduction in battery size. The framework uses HOMER to model microgrids that are cost optimized for a given CS and installation location. As the first step of the redesign, load prioritization is used to resize the system into a new configuration, termed as Default System Size (DSS). The battery size in DSS is subsequently optimized over second and third stages of design. The second stage, Battery Threshold Management (BTM) uses efficiencies brought about by setting battery thresholds using load information for a 24-hour autonomy. In the third step - Prediction Management (PM), the operational efficiency brought about by solar generation prediction data is incorporated into the design framework. The proposed framework is validated using 6 individual microgrids set in different rural Indian locations.
KW - battery storage
KW - capacity shortage
KW - design optimization
KW - forecasting
KW - load prioritization
KW - Microgrid design
KW - renewable energy sources
UR - http://www.scopus.com/inward/record.url?scp=85047730771&partnerID=8YFLogxK
U2 - 10.1109/GHTC.2017.8239275
DO - 10.1109/GHTC.2017.8239275
M3 - Conference contribution
AN - SCOPUS:85047730771
T3 - GHTC 2017 - IEEE Global Humanitarian Technology Conference, Proceedings
SP - 1
EP - 5
BT - GHTC 2017 - IEEE Global Humanitarian Technology Conference, Proceedings
PB - Institute of Electrical and Electronics Engineers (IEEE)
T2 - 7th IEEE Global Humanitarian Technology Conference, GHTC 2017
Y2 - 19 October 2017 through 22 October 2017
ER -
