Six indicators from the PVSyst manual to inform revenue estimates

Table of Contents

• In revenue estimates, look not only at power generation but also at the relationships between indicators.

• Indicator 1: Confirm the starting point for electricity sales and revenue calculations in E_Grid

• Indicator 2: Use specific power generation to assess earning power per unit of installed capacity

• Indicator 3: Assess the validity of design conditions and operational losses using PR

• Indicator 4: Read the assumptions for solar radiation conditions using GlobInc and GlobEff

• Metric 5: Break down the loss components that reduce revenue

• Indicator 6: Interpret cash flow using monthly energy and self-consumption rate

• Consistency of settings to check before using for revenue estimates

• Practical approach to using the PVSyst manual for profitability decision-making

• Improving the accuracy of on-site conditions increases the reliability of revenue estimates.

In revenue estimates, consider not only power generation but also the relationships among indicators

In revenue estimates for solar power projects, judging solely by the annual energy production can lead to misjudging the quality of a project. Even if the production appears the same, future revenue risk can vary greatly depending on whether it is generating because irradiance conditions are favorable, because the system design is efficient, or because significant losses have been overlooked. The purpose of reading the PVSyst manual is not simply to memorize operating procedures, but to correctly link the indicators that appear in simulation results to the revenue estimates.

In PVSyst, you can review many indicators useful for revenue assessment, such as generated energy, solar irradiation, performance ratio, losses, grid-injected energy, and values related to self-consumption. For example, `E_Grid` is treated as the amount of energy injected into the grid, and `PR` is a representative indicator that shows the relationship between the actually available generated energy and the expected generation based on incident solar irradiation and system capacity. These directly tie into income calculations and the verification of design validity.

In revenue estimates, you first look at "how much electricity will be generated." However, in practice, that alone is not enough. You must check "what assumptions produced that generation estimate," "where losses are occurring," "whether it affects revenue from electricity sales or the benefits of self-consumption," and "whether monthly revenue fluctuations can be sustained from a cash-flow standpoint," and only then do the numbers become usable for investment decisions.

When using the PVSyst manual, it is more important to focus on how to read the figures in the result tables and reports than on where to enter data on the screen. Indicators that should be used in revenue estimates should not be viewed in isolation. By combining E_Grid, specific yield, PR, irradiance, the loss breakdown, and the relationship between monthly energy output and self-consumption, you can convert a generation simulation into a revenue model.

Indicator 1: Confirm the starting point for power sales and revenue calculations in E_Grid

The first metric to look at in a revenue estimate is E_Grid. E_Grid indicates the amount of electrical energy injected into the grid in a standard grid-connected system. In sell-power projects, it is treated as the primary energy quantity to which the selling price is applied. In PVSyst’s simulation variables, E_Grid is described as "Energy injected into the grid".

The basis of revenue calculation is to view annual revenue from power sales as "the amount of electricity sent to the grid × the selling price per unit." Therefore, when reviewing PVSyst results, you should first check whether the annual E_Grid matches the annual power sales volume assumed in the business plan. What is important here is that the total amount of electricity produced by the generation equipment is not necessarily the same as the amount of electricity that actually counts toward revenue. The energy generated on the DC side goes through temperature losses, mismatch losses, wiring losses, inverter losses, AC-side losses, output curtailment, shutdown losses, and so on, and ultimately is allocated to either power sales or self-consumption.

For example, even if the output of a solar PV array appears high, E_Grid may not increase due to inverter capacity limits or grid-side constraints. In such cases, you cannot categorically say in revenue estimates that "increasing installed capacity will increase revenues." Rather, you need to compare the additional revenue from oversizing with the energy lost due to clipping.

Also, when checking E_Grid, it is important to look not only at annual values but also at monthly values. Even if the annual generation is sufficient, the impact on revenue can change depending on the selling price of electricity (feed-in tariff), the demand curve, electricity contracts, and the timing of output curtailment. In projects that include self-consumption in particular, surplus can increase in months with low daytime demand, while the effect of reducing purchased power can be greater in months with high demand.

When checking E_Grid while reading the PVSyst manual, it is practical to treat this value not merely as generated energy but as "the amount of electricity that ultimately converts into revenue." In feed-in (sell-to-grid) systems it forms the basis for sales revenue, and in self-consumption systems it relates to the handling of surplus sales and grid injection. For business plans for financial institutions, internal approval documents, and investor presentation materials, building the revenue logic starting from E_Grid makes explanations easier to understand.

Indicator 2: Using specific yield to assess earning potential per unit of installed capacity

The next indicator to check is specific yield. Specific yield is an indicator that shows how much electricity is produced per unit of installed capacity. It is commonly expressed in units such as kWh/kWp and is useful for comparing power plants of different sizes. While it is natural that projects with larger installed capacity will have higher annual generation, looking at the specific yield allows you to determine whether a system is efficiently generating revenue relative to its capacity.

PVSyst’s normalized performance metrics are organized in a way that makes them easy to compare based on incident energy and the nominal capacity of the installation. According to PVSyst’s documentation, these metrics are related to the energy incident on the plane and the array’s nominal installed capacity under STC conditions.

In revenue estimates, using specific power generation makes it easier to compare candidate sites. For example, suppose Site A has a large annual generation and Site B has a small annual generation. However, Site A may simply have a larger installed capacity, and Site B may have higher generation efficiency per unit of capacity. In this case, in terms of investment efficiency—including land costs, development costs, grid connection costs, and maintenance costs—Site B may be more advantageous.

Specific yield is also useful for initial-stage project screening. If the annual generation per 1 kWp of installed capacity is lower than expected, it can be assumed that some factor among solar irradiation conditions, orientation, tilt, shading, temperature, PCS capacity, layout density, or loss settings is reducing profitability. Conversely, if the specific yield is extremely high, you should check whether the assumptions or settings are overly optimistic.

What you need to be careful about here is not judging profitability solely by specific yield. Even if specific yield is high, high construction costs will delay payback. Conversely, even with an average specific yield, if you can use the roof of existing facilities and avoid land costs, profitability can be high. After understanding the meaning of specific yield in the PVSyst manual, it is necessary to view it in combination with CAPEX, OPEX, the feed-in tariff, the electricity purchase price, and curtailment risk.

Specific power generation is an indicator for reading the "efficiency" of revenue. If E_Grid is an indicator that looks at the total amount of revenue, specific power generation is an indicator that shows how much is earned relative to installed capacity. By placing these two side by side, it becomes easier to choose projects that are efficient investments rather than simply large projects.

Indicator 3: Assess the validity of design conditions and operational losses using PR

PR is an indicator commonly used in the design evaluation of photovoltaic power generation. In PVSyst’s description, PR is explained as the concept of comparing the energy actually and effectively produced with the energy expected from incident solar irradiance and system capacity. In a standard grid-connected system, E_Grid is used as the available energy.

PR is important in revenue estimates because it indicates the efficiency behind power generation. For example, even if annual power generation is large, you cannot tell from generation alone whether that is simply because the site receives high solar irradiance or because the design is superior. By looking at PR, you can understand how effectively the system converts available solar irradiance into electrical power.

When the PR is low, you need to carefully verify the causes when estimating revenue. A low PR can be caused by temperature losses, shading, soiling, mismatch, wiring losses, inverter losses, downtime losses, excessive clipping, and so on. Whether these losses are realistically unavoidable or can be improved through design changes will affect the assessment of business viability.

On the other hand, a high PR does not necessarily mean a good project. Even in regions with low solar irradiance, PR can appear high if losses are small. Therefore, PR needs to be evaluated alongside E_Grid and specific yield. Focusing only on PR can lead to optimizing for efficiency rather than revenue.

In practice, it is useful to use PR as an indicator for comparing design proposals. On the same site, create multiple cases that vary panel tilt, orientation, array spacing, PCS capacity, wiring routes, and shading measures, and compare E_Grid and PR side by side. In cases where E_Grid increases but PR falls significantly, it may be that losses have increased along with the added capacity. Conversely, even if PR drops slightly, if E_Grid increases substantially and returns improve relative to the investment, the project can be advantageous from a business perspective.

When understanding PR in the PVSyst manual, it is more important to focus on how to use the metric for revenue decisions than to memorize the indicator itself. PR is a metric for checking the quality of power generation. The higher the projected revenue appears, the more you need to examine the PR to verify why that revenue would be achieved.

Metric 4: Read the assumptions of solar irradiance conditions from GlobInc and GlobEff

Revenue from solar power generation is largely determined by irradiance conditions. In PVSyst results, GlobInc and GlobEff are important. GlobInc is treated as the value indicating the irradiance incident on the collecting plane, that is, the photovoltaic module plane. GlobEff relates to the understanding of the effective irradiance that reflects the influences of shading, IAM, and similar factors. In PVSyst sample reports and variable descriptions, GlobInc is shown as "Global incident in coll. plane", and GlobEff as "Effective Global, corr. for IAM and shadings".

When assessing solar radiation for revenue estimates, it's important not to judge based solely on horizontal-plane solar irradiance. In practice, solar panels are installed with specific tilt and azimuth settings. Therefore, what matters for power generation is how much solar radiation is incident on the panel surface. By looking at GlobInc, you can understand how orientation and tilt settings affect revenue.

Furthermore, by looking at GlobEff you can see how much the effectiveness of the incident solar radiation is reduced by shading and the angle of incidence. In projects with mountains, buildings, trees, shading between racking rows, or surrounding structures, GlobEff can be lower even when GlobInc is sufficient. If this difference is large, revenue estimates need to carefully consider the downside risk of reduced power generation.

Solar irradiation conditions are the very premise of revenue estimates. Even if the feed-in tariff and construction cost settings are correct, if the solar radiation data or terrain conditions deviate from the actual site conditions, the power generation forecast will be inaccurate. Especially in mountainous areas, developed/graded sites, slopes, lands with surrounding elevation differences, snowy regions, and coastal areas, it is important to consider not only simple solar radiation data but also terrain, orientation, tilt, shading, and temperature conditions together.

When using the PVSyst manual to check GlobInc and GlobEff, instead of simply accepting irradiance as "natural conditions," it is easier to make revenue judgments if you separate factors into those that can be improved by design and those that are difficult to improve. Orientation and tilt, array spacing, and layout can be adjusted in the design. On the other hand, surrounding topography and the reduction in irradiance during winter cannot be easily changed. Reducing losses that can be improved and conservatively reflecting conditions that cannot be changed in revenue estimates leads to more realistic projections.

Metric 5: Decompose the factors that lower revenue in the loss breakdown

An important perspective in the PVSyst manual to apply to revenue estimation is the breakdown of losses. In power generation simulations, the theoretical solar energy available does not directly translate into revenue from electricity sales. Generated output is reduced by various factors such as photovoltaic modules, wiring, inverters, transformers, grid constraints, outages, soiling, shading, and so on.

The simulation variables in PVSyst include several loss-related items that affect revenue, such as AC-side wiring losses, external transformer losses, system downtime losses, and grid limitation losses. By reviewing these, it becomes easier to trace at which stage the energy is being lost before reaching E_Grid.

In revenue estimates, it is important not to look at losses only as a total value, but to determine which losses have room for revenue improvement. For example, if temperature losses are large, review of module characteristics, racking height, ventilation conditions, and installation method should be considered. If shading losses are large, changes in layout, adjustment of row spacing, treatment of surrounding obstacles, and verification of site development plans are necessary. If wiring losses are large, there is room to review cable length, cross-sectional area, PCS placement, and collection methods.

The loss breakdown helps not only with investment decisions but also with prioritizing design improvements. Increasing installed capacity to raise energy production requires additional investment. On the other hand, if losses can be reduced while keeping the same installed capacity, profitability can be improved more efficiently. In particular, shortening wiring runs, revising PCS placement, arranging layouts that are less affected by shading, and suppressing excessive clipping are worth considering at the design stage.

Also, the breakdown of losses affects estimates of maintenance costs and operational risk. If soiling losses are underestimated, actual generation will underperform in operation, leading to shortfalls in revenue projections. If downtime losses and the non-operational rate are downplayed, actual cash flow will be worse than the simulation. In revenue projections, it is important to read losses not only as numbers that reduce generation but also as signals of future operating costs and risks.

Practitioners using the PVSyst manual should, while reviewing the loss diagrams and loss items in the report, clarify which losses are due to natural conditions and which can be improved through design, construction, or operation. Once this clarification is made, the task shifts from a mere simulation check to a design review aimed at increasing revenue.

Metric 6: Interpreting Cash Flow from Monthly Electricity Generation and Self-Consumption Rate

The sixth metric is the values for monthly energy generation and self-consumption. Even if annual generation is the same, how revenue is realized differs month by month. In the feed-in model, seasonal variations in solar irradiance and output curtailment affect cash flow. In the self-consumption model, whether the timing of generation aligns with the timing of demand determines the effectiveness of electricity bill reductions.

In PVSyst, when dealing with self-consumption, values such as E_User, E_Grid, EFrGrid, E_Solar, and SolFrac are described. E_Solar is described as the energy supplied from solar to the demand, and SolFrac is described as the fraction of demand supplied by solar. These are important for revenue estimates of self-consumption projects.

In projects where the feed-in tariff is fixed, revenue increases as E_Grid grows. However, in self-consumption systems, reducing purchased electricity can sometimes have a greater economic effect than selling surplus. In such cases, E_Solar and SolFrac may influence profitability more than the simple magnitude of E_Grid. In other words, for self-consumption systems, not only "how much was generated" but also "how much of the generated electricity was used in-house" is important.

By examining monthly electricity volumes, you can identify revenue imbalances that the annual total hides. For factories with high summer demand, logistics facilities that operate during weekday daytime, sites with low weekend demand, and facilities with high winter demand, the self-consumption benefits of the same solar installation will differ. When using PVSyst results for revenue estimation, it is important not to simply divide the annual energy by 12, but to match monthly generation with monthly demand.

Additionally, monthly figures are useful from a financial perspective. If power generation revenues or electricity savings are seasonally skewed, you need to check whether they align with the timing of expenditures such as loan repayments, lease fees, maintenance costs, insurance premiums, and taxes. This is because even if the annual balance is positive, cash flow in specific months may become tight.

When checking self-consumption–related indicators in the PVSyst manual, be aware that the indicators emphasized differ between feed-in (export-oriented) and self-consumption systems. For feed-in systems, E_Grid and monthly exported energy are central. For self-consumption systems, it is important to combine E_Solar, SolFrac, E_User, EFrGrid, and the surplus E_Grid to separately estimate electricity bill savings and feed-in revenue.

Consistency of settings to confirm before using for revenue estimates

Before using PVSyst results for revenue estimates, you need to confirm that the input settings and the assumptions of the business plan are aligned. No matter how carefully you read the metrics, if the input assumptions are off the revenue estimate cannot be trusted. In particular, check the meteorological data, installation site, orientation, tilt, system capacity, PCS capacity, loss settings, shading settings, availability/operational rate, grid constraints, and self-consumption demand data.

First, verify the consistency between the meteorological data and the installation site. Solar irradiance is the fundamental determinant of power generation. If meteorological data that differ from the installation location are used, GlobInc or E_Grid may not reflect actual conditions. Topographical differences, elevation differences, whether the site is coastal or inland, and the presence or absence of snow or fog also affect long-term power generation.

Next, we will examine the relationship between installed capacity and PCS capacity. When assuming oversizing, you need to look at the balance between the increase in E_Grid and clipping losses. Increasing installed capacity tends to raise generation during low-irradiance periods, but it can also increase the power clipped by the PCS at peak times. In the revenue calculation, compare the additional panel costs with the additional revenue to determine whether the investment efficiency is high.

Shadow settings are also important. Underestimating nearby shading or distant terrain can make GlobEff and the loss breakdown appear optimistic and may cause E_Grid to be overestimated. At sites where shading occurs during periods of low solar altitude, monthly winter generation can have a large impact on revenue and self-consumption benefits. Because this cannot be seen from annual values alone, monthly values should be checked together with the loss breakdown.

In self-consumption systems, the accuracy of demand data affects revenue estimates. Facilities with stable daytime demand tend to have higher self-consumption rates, but facilities whose demand falls on holidays or seasonally may experience increased surplus. Overestimating the self-consumption rate can overstate electricity bill savings and make the investment payback period appear shorter.

PVSyst includes an economic evaluation framework that covers electricity sales prices and self-consumption. According to PVSyst's documentation, electricity sales pricing strategies are used for calculating revenues and evaluating profitability over the project period. However, for final investment decisions, it is necessary to separately organize, in addition to PVSyst outputs, power contracts, taxation, interest rates, maintenance costs, replacement costs, insurance, land costs, grid connection costs, and other related items.

Practical workflow for applying the PVSyst manual to profitability assessments

To leverage the PVSyst manual for revenue assessment, it is more efficient not just to follow the interface screens in order but to establish a workflow for reading the results. First, confirm the revenue-eligible energy in E_Grid, then check the specific yield to assess efficiency per unit of installed capacity. Next, check PR to determine whether the generation is reasonable given the irradiance conditions. Further, verify the irradiation and shading assumptions with GlobInc and GlobEff, and use the loss breakdown to decompose the factors that are reducing revenue. Finally, review the monthly energy and self-consumption indicators to check not only annual revenue but also any imbalance in cash flow.

Viewed in this light, the magnitude of power generation alone becomes less decisive. For example, even if E_Grid is large, if PR is low, losses are high, and the incremental increase in returns from additional investment is small, it cannot be said to be highly commercially viable. Conversely, even if E_Grid is somewhat smaller, in cases where the specific yield is high, losses are low, and construction and maintenance costs can be kept down, return on investment can be stable.

When presenting internally, rather than simply pasting the PVSyst results, it is clearer if you verbally organize how they correspond to the revenue estimates. E_Grid is the basis for revenue from power sales, specific yield describes revenue efficiency per unit of installed capacity, PR indicates design efficiency, GlobInc and GlobEff represent the irradiation assumptions, the breakdown of losses shows room for improvement, and monthly energy generation and self-consumption rate are used to check cash flow. Dividing the roles in this way makes it easier to explain to stakeholders outside the specialty.

When comparing multiple cases, always verify the reasons for changes in the metrics. Check how GlobInc changes when you alter the azimuth, how shading loss and land use efficiency change when you widen the array spacing, and how E_Grid and clipping loss change when you change the PCS capacity. Being able to explain the causal relationship between design changes and metric changes—not just the resulting numbers—adds persuasive power to the revenue estimate.

Also, when preparing revenue projections it is important to create conservative scenarios as well. By changing conditions that affect revenue — for example, if solar irradiation is lower than expected, soiling losses increase, downtime losses increase, output curtailment occurs, or electricity tariffs change — and analyzing the sensitivity, you can grasp the risks to investment decisions. PVSyst’s indicators can be used not only to produce a single forecast value but also to explain risk.

Improving the accuracy of site conditions leads to increased reliability of revenue estimates

Even if you understand the six indicators in the PVSyst manual used for revenue estimation, the reliability of the simulation falls if your understanding of site conditions is rough. Solar power revenue is not determined solely by desk-based settings. Site elevation differences, slopes, surrounding buildings, trees, existing structures, land development plans, access roads, racking layout, cable routes, and the way shadows fall—conditions present at the site—affect power generation and losses.

In particular, terrain and shading conditions directly affect revenue estimates. If the on-site height relationships are not correctly reflected, estimates of GlobEff and shading losses will be off. If panel layout or racking heights differ from reality, inter-row shading and wiring lengths will also change. These ultimately feed into E_Grid, PR, the loss breakdown, and monthly energy output, altering projections for feed-in revenue and self-consumption benefits.

Therefore, both before and after performing revenue estimates in PVSyst, on-site surveys and the organization of coordinate information are also important. Accurately identifying site boundaries, elevation, obstacles, installable areas, azimuth, and tilt will improve the accuracy of the conditions entered into PVSyst. As the accuracy of input conditions improves, the reliability of E_Grid, specific yield, PR, GlobInc, GlobEff, the loss breakdown, and self-consumption–related values will also increase.

In practice, simulation engineers, design engineers, on-site surveyors, and revenue estimators may work separately. If their understanding of site conditions is misaligned, the results from PVSyst and the business plan will not match. For example, if panels were placed in areas that are unusable on site, if the heights of surrounding obstacles were not accounted for, if post-development ground elevations were not used, or if there were discrepancies in the handling of azimuths or coordinates, the assumptions behind the revenue estimate will be undermined.

When reading the PVSyst manual, it is important to look not only at the on-screen indicators but also at the site data that support those indicators. E_Grid is the starting point for revenue from electricity sales. Specific yield indicates the earning power per unit of capacity. PR shows the design efficiency. GlobInc and GlobEff indicate the assumptions for solar irradiation and shading. The loss breakdown decomposes the causes of revenue decline. Monthly energy production and the self-consumption rate provide clues for reading cash flow. By using these indicators correctly, PVSyst results become not just technical reports but revenue documents usable for investment decisions.

If you want to carry out everything from site surveys to design, PVSyst simulations, and revenue estimation with consistently high accuracy, using high-precision positioning that can be handled on a smartphone is also effective. Using LRTK (iPhone-mounted GNSS high-precision positioning device) makes it easier to organize on-site location and elevation information, and helps with layout considerations for solar power plants, checking terrain conditions, understanding shadows and obstacles, and sharing design assumptions. To apply the six indicators read from the PVSyst manual to revenue estimates, it is important not only to rely on the simulation numbers but also to improve the accuracy of the on-site conditions that support those numbers.