Individual Project Technical or Techno-Economic Analysis

This foundational DPV analysis is performed for individual DPV systems, often from the customer or installer’s perspective. It examines either the expected technical performance or the combined technical and economic (i.e., techno-economic) performance of an individual DPV system. In practice, this means analyzing the expected output of a DPV system, understanding the extent to which DPV production aligns with the customer’s consumption over time, and determining how much value the system creates for the customer if retail electricity purchases are avoided and/or if electricity is sold back to the utility. These analyses tend to be projections of expected performance rather than evaluations of existing systems. More detailed techno-economic analyses may account for the time-value of money and examine project performance over 10-year to 20-year periods or longer.

Technical performance analyses can use anything from simple performance assumptions (e.g., assuming a flat annual capacity factor and annual customer demand) to a highly detailed technical performance simulation (i.e., accounting for sub-hourly solar resource and demand data, specific panel and inverter technologies, panel orientation and azimuth angle, and shading). The analyses can correspondingly produce anything from gross annual generation estimates to more detailed sub-hourly profiles of generation for a DPV system. When data are available that match those estimates against customer electricity consumption patterns, they can be used to calculate how much DPV is being consumed on-site versus exported back to the grid.

Techno-economic performance analyses rely on the same range of technical performance analysis assumptions and methods, but they also incorporate economic considerations. For instance, rather than just tracking how much DPV—in kilowatt-hours (kWh)—from a rooftop system are consumed on-site by the customer versus exported to the grid, techno-economic performance analysis assigns a financial value to those quantities and calculates relevant financial metrics such as bill savings, payback period, and net present value. With respect to economic performance estimation methods, analysts can rely on simple economic assumptions (e.g., all DPV generation offsets an average volumetric tariff) to more complex calculations (e.g., each hour of DPV generation is matched to hourly consumption data under a time-of-use net billing tariff).

Prospective DPV customers or financial institutions can use these analyses to determine the economic viability of an investment in a DPV system. Similarly, DPV installers can use these analyses to prepare price quotes that demonstrate to their customer how soon a system might pay itself back. Policy, regulatory or utility analysts can use these analyses to feed into higher-level analyses, such as to a Ratepayer Impact Analysis or a Power System Operational Impact Analysis, or to characterize how a particular change in regulation or policy (e.g., a shift from net energy metering to net billing) may impact project economics for prospective DPV customers.

Importantly, both technical and techno-economic performance analyses underpin nearly all other DPV analyses. In most other DPV analyses, statistically representative or “average” customers must be formulated and scaled to higher levels of deployment in order to understand broader market impacts. This is considered a foundational step in most DPV analyses.

The System Advisor Model (SAM), which is developed by NREL and is free, is a powerful tool for simulating DPV generation profiles and financial metrics of DPV systems. Given typical weather data for a given city, which are available for many cities worldwide within the tool, the user can generate DPV generation profiles given several standard PV system characteristics, including system size, orientation, and technology types. In addition to the generation output—which is available hourly or subhourly if the input data are available at a finer resolution—SAM calculates a variety of financial metrics for the DPV customer given input costs, retail tariff information, and the specifics of the DPV compensation mechanism and other available financial incentives.

Examples of Analysis Questions:

Below is a non-exhaustive list of illustrative analysis questions for Individual Project Technical and Techno-Economic Analysis efforts.


  • How much generation can be expected for a specific DPV system in a given location on an hourly, daily, monthly or annual basis?
  • How would a different system design (e.g., panel orientation) or technology (e.g., inverter efficiency) impact DPV production for a specific system?
  • How much generation can a utility expect will be injected into the grid from a prototypical residential DPV system in a prototypical year? At what times of year or day will those injections occur?


  • What is the annual expected customer bill savings (and/or income from electricity injected into the grid) from a particular DPV system? What is the expected levelized cost of energy for the DPV system, the payback period, and the internal rate of return of the investment?
  • How would changing the system design (e.g., panel orientation) or technology (e.g., inverter efficiency) for a specific DPV system shift project economics?
  • How would changes to the DPV compensation mechanism impact project economics for a prototypical residential customer or business?
  • Which classes of retail customers are likely to have the highest bill savings from installing a DPV system?
  • How would shifting policy and regulatory frameworks affect the project economics for a prototypical customer or business?
  • How would a shift from an energy-based tariff to a demand charge impact how much revenue a utility collects from a prototypical commercial customer in a particular region of a country?
  • At what capital cost (in $/W) will a prototypical residential customer experience a payback period of seven years? 

Overview of Key Analysis Inputs, Assumptions, Outputs, and Tools:

Stakeholder Perspective Individual DPV system (i.e., customer or installer).
Key Input Data

Solar Insolation Data: a measure of the solar resource, often using historical or typical meteorological year (TMY) data; can be used to formulate DPV production estimates. In simpler analyses, annual or monthly capacity factors can be assumed.

Metering and Billing Arrangement: a description of how consumption- and DPV generation-related electricity flows are measured and billed, including net energy metering (NEM), buy-all sell-all (BA-SA), and net billing (NB).

Retail Electricity Tariff: For NEM and NB arrangements, retail electricity purchases can be avoided via self-consumption of DPV. In such cases, the retail electricity tariff must be accurately characterized for the particular customer class in order to estimate avoided costs from the DPV system.

DPV Sell Rate: the level of compensation a DPV customer receives for injected electricity into the grid; can take the form of a bill credit (in units of kWh or cash) with specific limitations of use (e.g., expiration dates), or simply a cash payment each billing cycle or year. Sell rates can stay the same over time, change based on time of production, or increase/decrease over multiyear periods.

Customer Demand Data: For NEM and NB customers, DPV generation can be used to offset customer demand; thus, gross customer demand data are a relevant quantity for economic calculations, and also for technical calculations of how much DPV is self-consumed versus injected to the grid.
Key Input Assumptions

DPV System Size: The size of the modeled DPV system strongly impacts production estimates.

DPV System Performance Assumptions: The efficiency of the PV module to produce electricity, as well as the efficiency of the inverter to convert DC to AC energy, impact the technical performance of the DPV system, along with several other DPV performance assumptions 

Technology Cost Assumptions: The initial capital cost of the system, as well as the expected operation and maintenance (O&M) costs, strongly influence the customer economics of a DPV investment

Tariff Rate Escalation Rate: The assumed annual increase in the retail electricity tariff level can impact project economics for NEM and NB customers.

Loan Assumptions: If a DPV customers take out a loan to fund the upfront cost of their system, the characteristics of the loan terms (e.g., interest rate, and term) can be included in techno-economic calculations.

Discount Rate: If a DPV customer invests their own capital to purchase the system, the discount rate associated with their capital can be included to represent the value that their capital could otherwise be yielding were it not invested in the DPV system. In this context, the discount rate can also be thought of as an “opportunity cost” of capital for the DPV investor.

Inflation Rate: For techno-economic analyses that examine project performance over multiple years, the inflation rate of the economy can impact project economics.

Key Outputs

DPV Gross Production: amount of electricity a DPV system will generate in a given period of time (ranging from minutes to years).

DPV Self-consumption: amount of DPV energy that is consumed on-site at the moment of generation.

DPV Grid Injections: amount of DPV energy that is injected into the electricity grid.

Bill Savings: amount of money a customer saves on their electricity bill because of DPV generation, relative to a no DPV scenario, during a given billing cycle or year.

Simple Payback Period: amount of time after which a DPV system is expected to accrue enough bill savings to offset the initial cost of the DPV system to the owner/investor.

Net Present Value: a measurement of project profitability that subtracts the capital and operational expenditures from the value that a project accrues over time, adjusted for the time-value of money.

Tools and Models

System Advisor Model/PVWatts (NREL)

PVsyst (PVsyst SA) 

PVlib (Open Source)

Discussion and Practical Considerations

Who can conduct this analysis? How costly and time-intensive is it to conduct?

Individual project technical and techno-economic analyses are among the simplest, least expensive, and least time-intensive DPV analyses to conduct. A basic knowledge of engineering or finance concepts is useful background; a knowledge of how retail electricity rates and metering and billing arrangements can be structured may be required for more advanced analyses. Many existing tools offer graphical user interfaces that help streamline data input, quantitative analysis, and interpretation of results.

What are the key challenges to getting these analyses right?

As with all other DPV analyses, adequate data availability can be a challenge. Sufficiently detailed customer demand data tends to be the most difficult type of data to acquire, as it is not always collected by utilities in developing country settings for a variety of reasons, and the data can often be confidential in nature.

What are some key practical tips to keep in mind?

Detailed customer electricity demand data should be collected early and often. Any techno-economic analysis for DPV customers under a net billing scheme requires detailed (i.e., hourly or subhourly) demand in order to accurately capture the relevant physical flows of electricity to and from the grid. This is also true of NEM schemes where customers are under a time-of-use tariff and/or pay a demand charge. Analysis of DPV customers under NEM schemes with time-invariant tariffs only requires demand data at the fidelity of the billing cycle (i.e., monthly). Analysis of BA-SA customers may require billing cycle level data, though under some circumstances, it may not require demand data at all. 

In addition, it is important to remember that your analyses are only as accurate as the least detailed or least accurate data set. Analyses are commonly limited by customer demand data (see above) or incomplete information about DPV system pricing, particularly in newer markets for DPV. For the latter, informal discussions with individual developers may be the best tactic to begin to understand expectations of pricing, though these projections should be appropriately vetted and internationally benchmarked (while adjusting for local conditions) whenever possible. Another approach is to perform sensitivity analysis using both low and high estimates of system pricing, and to present relevant analysis results as ranges. 

How does this analysis relate to other DPV analyses?

Both technical and techno-economic performance analyses underpin nearly all other DPV analyses. In most other DPV analyses, statistically representative or “average” customers must be formulated and scaled to higher levels of deployment in order to understand broader market impacts. This is considered a foundational step in most DPV analyses.

Examples of Analyses

  1. Informing Mexico’s Distributed Generation Policy with SAM Analysis. (Español). 2018. U.S. Agency for International Development.
  2. Impact of Rate Design Alternatives on Residential Solar Customer Bills: Increased Fixed Charges, Minimum Bills and Demand-based Rates. 2015. National Renewable Energy Laboratory.
  3. Distributed Photovoltaic Economic and Technical Impact Analysis in the Philippines. 2019. National Renewable Energy Laboratory and U.S. Agency for International Development. 
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