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Economic Analysis of Battery Storage Integration with Solar Panels for Residential Energy Saving
Purpose of the Project
Purpose of the Project
The purpose of this model is to analyze the economic viability of installing a battery storage system to complement the existing solar panel setup for Naomi. The model aims to determine the potential savings in electricity costs by storing excess solar electricity for later use and to calculate key financial metrics such as Net Present Value (NPV) and Internal Rate of Return (IRR) under different scenarios.
Data
Data
The data provided consists of hourly measurements of solar electricity generation and electricity usage for the year 2020. Key columns include:
Hour: Hour of the day (0 to 23)
Date/hour start: Timestamp
Solar electricity generation (kWh): Amount of solar electricity generated each hour
Electricity usage (kWh): Amount of electricity used each hour
Outlier Detection
Data Checks and Actions Taken
Data Checks and Actions Taken
Completeness Check: Verified that data has 8760 entries corresponding to each hour of 2020, excluding February 29.
Outlier Detection: Identified and addressed significant outliers by trimming extreme values from both ends.
Assumptions Used
Assumptions Used
Initial Conditions: Battery charge level starts at 0 kWh on January 1, 2020, at 00:00.
Battery Specifications: The battery can store a maximum of 12.5 kWh and a minimum of 0 kWh.
Electricity Prices: $0.17 per kWh in 2022, with annual price inflation scenarios of 4% (Scenario 1), and 4% increasing by 0.25% per year (Scenario 2).
Rate for NPV: A discount rate of 6% per annum is used.
Electricity Usage Priority: Current solar generation is used first, followed by battery-stored electricity, and finally electricity bought from the provider.
Methodology
Methodology
Data Preprocessing:
Converted the datetime index to ensure proper time-based operations.
Resampled data to monthly values for aggregate calculations and visualizations.
Calculations:
Bought Electricity (without battery): For each hour, calculated as the difference between electricity usage and solar generation, subject to a minimum of zero.
Excess Solar Electricity: For each hour, calculated as the difference between solar generation and electricity usage, subject to a minimum of zero.
Battery Charge Level: Modeled the cumulative charge level of the battery, considering the excess solar electricity available for storage and the usage from the battery.
Bought Electricity (with battery): For each hour, calculated based on remaining electricity needs after utilizing solar generation and battery storage.
Monthly Aggregation:
Aggregated hourly data to monthly totals for solar generation, electricity usage, and electricity purchased with and without the battery and visualized the data.
Financial Metrics (On Excel):
NPV Calculation: Discounted future annual savings from using the battery, under two scenarios of electricity price increases, using Excel’s NPV function.
IRR Calculation: Used Excel's IRR function to determine the rate at which the NPV of savings equals the initial investment cost of the battery.
Results
Results
Monthly Aggregations:
Saving
Saving
The results of the data analysis process show that installation of a 12.5 kWh battery greatly reduce the amount of electricity bought throughout the year by $ 692.6 as computed.
Financial Metrics (On Excel):
As shown below, the calculated NPV is positive for both scenarios, and the IRRs are greater than the discounting rate of return, meaning that the project is viable in both scenarios.
Tools and Technologies
Tools and Technologies
Python, Jupyter Notebook, Microsoft Excel
Pandas, Numpy, NPV, IRR
Questions?
Questions?
Contact antonnm7@gmail.com to get more information about the project
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