IMPACT OF SOLAR PV-BATTERY SYSTEM TO APPROACH NET ZERO ENERGY BUILDING BASED ON PMV
Heating, Ventilating, and Air Conditioning (HVAC) systems takes large part of electrical consumption in building particularly if temperature setpoint is cold in long duration. Therefore, controlling HVAC is important to reduce power consumption. But, reducing temperature can change thermal comfor...
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| Format: | Final Project |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/55251 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Heating, Ventilating, and Air Conditioning (HVAC) systems takes large part of electrical
consumption in building particularly if temperature setpoint is cold in long duration. Therefore,
controlling HVAC is important to reduce power consumption. But, reducing temperature can
change thermal comfort in a building. There is an alternatives to keep thermal comfort by
maintaining Predicted Mean Vote (PMV) at certain values. PMV values is optimized from HVAC
usage in setpoint 18?C. PMV is optimized by changing setpoint hourly until PMV reached best
range according to ASHRAE recommendation. This method can decrease HVAC usage so the
total load is decreasing 114 kWh in a year. Optimized demand is used to model electricity system
in building and measuring the Net Zero Energy (NZE) level. NZE level is calculated using
balance graphs and Load Matching and Grid Interaction (LMGI) method. Variation in system
are charging limit from grid with value of 95% and 65%, battery capacity with value of 1000 Ah
and 1200 Ah, and PV capacity with value of 3 kW and 5 kW. A system with PV capacity 4 kW,
battery capacity 900 Ah, and charging limit 80% is used as base case. Results show that the best
system configuration to achieve NZE is system with PV capacity 4 kW, battery capacity 900 Ah,
and charging limit 95%. This system has LOLP index 50.41%, LM 84.12%, NGI 49.59%, and
exported energy reaching 1.215 MWh in a year. To achieve NZE, it is suggested to use the
calculated capacity of PV and battery capacity and increasing charging limit in the optimum
point from grid because energy exported from PV is higher. Demand variation shows that
clothing insulation has higher standard deviation that occupancy. Building model is done using
eQUEST while the electricity model is using Python. |
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