Solar power generation simulations are not just documents for checking annual generation. In practice, the business case, investment decisions, design policy, internal explanations, and the persuasiveness of approval documents change significantly depending on how much of the simulated generation is self-consumed and how much can be sold. For practitioners who search for "solar power generation simulation," what matters is not the generation numbers themselves but calmly judging how those numbers translate into feed-in revenue.

# Table of Contents

• Basics of reading feed-in revenue from solar power generation simulations

• Tip 1: Look at sellable energy, not generation

• Tip 2: Check monthly surplus energy

• Tip 3: Read self-consumption rate and export ratio separately

• Tip 4: Identify conditions that tend to overestimate feed-in revenue

• Tip 5: Reassess generation accuracy based on site conditions

• Points practitioners should be careful about when reading feed-in revenue

• Using feed-in revenue simulations to inform design decisions

• Summary

# Basics of reading feed-in revenue from solar power generation simulations

When reading feed-in revenue from solar power generation simulations, the first point to understand is that generation and exported energy are not the same. If an annual generation figure is prominently displayed in the simulation results, it can give the impression that the entire amount will become feed-in revenue. In reality, part of the generated power will be used within the facility, part may be stored or controlled so it does not flow to the grid, and only a portion is sold. In other words, simply looking at total generation is insufficient for assessing feed-in revenue.

The way electricity is used varies greatly depending on building type—residential, factory, warehouse, retail, office, or public facility. Facilities that use a lot of power during the daytime tend to self-consume solar generation, reducing the amount available for sale. Conversely, buildings with low daytime demand tend to have more surplus generation and a higher export ratio. Therefore, even with the same system capacity, region, and roof conditions, the appearance of feed-in revenue changes with demand patterns.

Feed-in revenue cannot be read correctly by simply multiplying annual generation by the feed-in tariff. You must consider the amount actually eligible for sale, contract conditions, generation losses after start of operation, equipment aging, the potential for output curtailment, and maintenance status. Especially for corporate projects, evaluation is comprehensive and includes not only feed-in revenue but also reduced electricity purchases through self-consumption, cost suppression from on-site use, securing power during emergencies, and responses to environmental value. Thus, rather than emphasizing feed-in revenue alone, it is important to break down how generated electricity will be used and explain that breakdown.

Practitioners reviewing simulations should first check annual generation, monthly generation, time-of-day generation trends, facility-side electricity demand, and the timing of surplus power. By overlaying these elements, you can see how stable feed-in revenue is likely to be, whether it is concentrated in certain seasons, and whether more generation than expected will be self-consumed. Reading feed-in revenue is not a matter of looking at the surface numbers of generation; it is an exercise in interpreting the relationship between generation and consumption.

# Tip 1: Look at sellable energy, not generation

The first tip for reading feed-in revenue is to look at sellable energy rather than generation. Solar generation simulations prominently show annual and monthly generation, but those figures represent projected energy produced by the system, not necessarily the energy that can be sold. To evaluate feed-in revenue, you need to confirm the amount of energy that can actually be sent off-site after subtracting self-consumption and losses from generation.

For example, if a factory’s equipment is operating during the daytime, solar-generated power will first be consumed on-site. In that case, even if generation is large, the amount sold may be limited. Conversely, facilities with little daytime operation tend to have more surplus generation, increasing sellable energy. Thus, when estimating feed-in revenue, it is essential to compare generation with the building’s and equipment’s electricity demand.

In practice, relying solely on the annual generation total to estimate revenue can lead to overestimation. The important thing is to check when and how much generated power becomes surplus. Solar generation is high during the day and nonexistent at night. Therefore, facilities with high daytime consumption prioritize self-consumption, while those with high nighttime consumption have less overlap between generation and demand. Overlooking this timing mismatch leads to unrealistic expectations for sellable energy.

Also, when assessing sellable energy, do not assume that adding system capacity will simply increase sales proportionally. Even if more panels can be installed on the roof or site, connection conditions, constraints of power receiving equipment, output curtailment, and facility operating conditions can limit the amount that can be sold. Simulations tend to show larger annual generation with more capacity, but that increase may not all translate into revenue.

To estimate sellable energy, separate generation, self-consumption, surplus energy, and potentially curtailed energy. Even if the simulation does not present these separately, it is important to estimate the amount that can be exported based on the facility’s historical electricity usage and operating hours. For feed-in revenue assessment, the certainty of the energy that can be sold is more important than the sheer volume of generation.

# Tip 2: Check monthly surplus energy

The second tip for reading feed-in revenue is to check monthly surplus energy. Looking only at annual feed-in revenue can give the impression of stable income. However, solar generation varies by season. Monthly generation is affected by solar irradiance, sunshine hours, temperature, weather, snowfall, the rainy season, typhoons, and surrounding shading. Feed-in revenue is similarly affected, so it is important to review not just annual totals but monthly trends.

When looking at monthly generation, note that months with high generation do not always correspond to months with high feed-in revenue. For example, summer often yields high generation but some facilities also increase electricity use for air conditioning, raising self-consumption and limiting sales. Conversely, during spring and autumn when demand is moderate, surplus energy may increase depending on the balance between generation and consumption.

Monthly sellable energy is determined by the combination of seasonal generation variability and facility-side demand variability. Practitioners should not only look at peaks and troughs in monthly generation but also overlay monthly usage to see when surplus tends to occur. For corporate facilities, busy seasons, slow seasons, holidays, extended shutdowns, and equipment stoppages affect export volumes. For factories this means production schedules, for stores business hours, and for schools or public facilities closure periods.

Checking monthly surplus energy lets you understand bias in feed-in revenue. If revenue is concentrated in a particular season, relying on the annual average may hide operational risks. For example, if output curtailment or equipment stoppage occurs during a high-generation period, annual revenue could be significantly impacted. Similarly, if generation falls more than expected during low-irradiance months, annual financial plans will be affected.

When reading monthly figures, check not only the month with the highest generation but also the months with the lowest generation. Feed-in revenue should be evaluated by including the declines in low months as well as the expectations for high months. Understanding monthly variability enables conservative revision of revenue estimates and clearer presentation of risks during internal briefings. The purpose of using solar generation simulations is not to present ideal income but to grasp a realistic income range.

# Tip 3: Read self-consumption rate and export ratio separately

The third tip for reading feed-in revenue is to separate the self-consumption rate and the export ratio. The economic effect of solar generation includes both revenue from sales and reduced electricity purchases from self-consumption. Both utilize generated energy, but their implications differ. Looking only at feed-in revenue may overlook the benefits of self-consumption, and conversely, emphasizing self-consumption alone can obscure realistic expectations for feed-in revenue.

The self-consumption rate is the proportion of generated energy used within the facility. The export ratio is the proportion of generated energy exported as surplus. Separating these two makes it clearer how the system creates value. A high self-consumption rate means generated energy is being used effectively on-site. A high export ratio means generated energy tends to flow off-site as surplus.

If the goal is to increase feed-in revenue, a higher export ratio may seem advantageous. However, in practice this is not always the case. Depending on facility electricity usage, contract conditions, and operational objectives, increasing self-consumption can sometimes yield greater overall economic benefit. Therefore when assessing feed-in revenue, evaluate it in combination with the effects of self-consumption.

Ideally, simulations should show how generation, consumption, and surplus overlap by time of day. Monthly totals alone may not accurately capture the relationship between self-consumption and export. For example, within the same month, weekdays may have higher self-consumption while holidays see more exports. Some facilities experience surplus during lunch breaks or planned production stoppages. Understanding these fine-grained time variations brings feed-in revenue estimates closer to reality.

Separating self-consumption rate and export ratio also helps determine system capacity. Increasing capacity generally raises generation but may also increase surplus that cannot be consumed. If that surplus can be exported, revenue follows, but connection or control constraints may prevent selling all surplus. Conversely, limiting capacity might reduce exports but increase self-consumption and result in a less wasteful design.

Practitioners should not aim to maximize feed-in revenue in isolation but should consider how generated energy can be used to maximize overall benefits. To do so, confirm self-consumption rate and export ratio separately and clarify which effect the project prioritizes. The skill to read feed-in revenue is not just the ability to read income figures but the ability to read the balance between power flows and system design.

# Tip 4: Identify conditions that tend to overestimate feed-in revenue

The fourth tip for reading feed-in revenue is to identify conditions that tend to overestimate revenue. Solar generation simulations are a useful decision-making tool, but results can vary significantly depending on input conditions and assumptions. Feed-in revenue in particular is sensitive to small changes in projected generation, sellable energy, and operating conditions. Therefore, do not accept simulation results at face value; check which assumptions inflate revenue.

First, watch for optimistic solar irradiation assumptions. Regional irradiation data are often treated as long-term averages, but actual year-to-year weather varies. Some years have more rain or cloud cover, while others are affected by typhoons, snowfall, yellow dust, or haze. A simulation may show standard generation, but actual operation could underperform due to weather. When reading feed-in revenue, consider not only average cases but also scenarios with lower-than-expected generation.

Next, do not underestimate shading effects. Shadows from nearby buildings, trees, utility poles, signs, rooftop equipment, and adjacent structures affect generation. Shadows lengthen during low solar altitudes at morning and evening or in winter, causing generation to drop more than expected. Losses from shading affect not only annual generation but also sellable energy; shading occurring in times when surplus would otherwise arise can reduce feed-in revenue.

Soiling and degradation of equipment are also easily overlooked. Because solar systems are outdoors, they are affected by dust, pollen, bird droppings, fallen leaves, salt damage, and snow. Inadequate inspection or cleaning can gradually reduce generation. Also, long-term performance declines with age and must be considered. Estimating revenue based only on first-year generation risks overestimating long-term returns.

Also pay attention to equipment downtime and maintenance. Simulations often assume continuous normal operation, but inspections, parts replacement, fault responses, communication issues, and work on the power receiving equipment can temporarily halt generation or exports. Even short outages can dramatically affect revenue if they coincide with high-generation periods or peak times.

To avoid overestimating feed-in revenue, check for unfavorable as well as favorable conditions. Compare standard, slightly conservative, and downside generation scenarios to grasp the range of revenue. In practice, using defensible conservative figures is more useful for internal explanations and decision-making than selecting the most attractive number. Feed-in revenue must be read including its uncertainties, not only expected values.

# Tip 5: Reassess generation accuracy based on site conditions

The fifth tip for reading feed-in revenue is to reassess generation accuracy based on site conditions. Solar generation simulations produce results based on input conditions, so if those inputs differ from site reality, revenue estimates will be off. To achieve accurate feed-in revenue estimates, verify roof, site, surrounding environment, electrical equipment, and operating realities—not just desk-based assumptions.

First, confirm the orientation and tilt of the installation surfaces. Panel direction and angle strongly affect generation. Even if drawings suggest adequate installation area, actual roof pitch, steps, obstructions, rooftop equipment, railings, lightning protection, and inspection walkways may limit installable area. If installation angles cannot be set as ideal, simulated and actual generation may differ.

Checking the surrounding environment is also essential. Nearby tall structures can create shadows that vary by season and time of day. Even if shadows seem minimal on site, conditions change in winter or at morning/evening. Trees can grow and expand future shading. Because feed-in revenue is evaluated over a long period, consider not only the current state but also future changes in the surrounding environment.

Electrical equipment conditions matter as well. Selling generated energy requires not only generation equipment but also receiving equipment, wiring, protection devices, metering, and grid connection conditions. Even if simulations show potential generation, actual connection conditions may require curtailment of part of the output. Also, facility usage patterns can shift the timing of surplus generation compared to assumptions.

When reassessing site conditions, do not rely solely on drawings; obtain accurate position, height, orientation, and obstruction information. Errors in roof shape or equipment layout affect the number of installable modules, shading extent, wiring plans, and inspection routes. As a result, generation estimates and feed-in revenue assessments change. For existing buildings, it is not uncommon for drawings to be outdated, renovation histories to be unreflected, or site conditions to differ from drawings.

Improving the accuracy of feed-in revenue estimates requires correcting simulation results with site verification. Generation simulations are a starting point; they become practical numbers only when they reflect site conditions. The person responsible for reading feed-in revenue must not simply trust simulation outputs but must verify that their assumptions match the site.

# Points practitioners should be careful about when reading feed-in revenue

When reading feed-in revenue, practitioners should be careful not to make decisions based solely on income figures. Feed-in revenue is an easy-to-understand metric and tends to attract attention in internal presentations. However, adopting solar generation requires considering many other factors in addition to feed-in revenue: the effect of self-consumption, equipment maintenance, inspection systems, roof and site safety, future equipment replacement, changes in power demand, and alignment with business plans.

Especially when feed-in revenue figures look large, checking assumptions becomes more important. Ask why projected generation is high, what justifies a high export ratio, whether surplus occurs stably, and whether operational changes could increase self-consumption. For example, future equipment additions that raise daytime demand could reduce current expected exports. Conversely, energy-efficiency upgrades or changes in operating hours could increase surplus.

Also, feed-in revenue should be considered over the long term, not only for a single year. Even if first-year generation is favorable, long-term generation can change due to equipment aging or changes in the surrounding environment. New rooftop installations, nearby building construction, tree growth, or changes in facility use can invalidate simulation assumptions. Therefore, when reading feed-in revenue, consider future variability in addition to current conditions.

In practice, treat feed-in revenue as a range of estimates rather than a single fixed value. Assuming standard generation, slightly lower generation, or changed operating conditions in advance provides flexibility in decision-making. For internal approvals or client presentations, presenting figures with clear assumptions and risks is more credible than showing only the highest revenue scenario.

Furthermore, those reading feed-in revenue should be able to check simulation input conditions. Do not accept only the annual generation result; confirm which regional irradiance data were used, how installation angles were set, how shading was accounted for, and how equipment losses were treated. Simulations with unclear inputs may look neat but are less useful for practical decision-making.

Reading feed-in revenue is about producing realistic figures for decision-making, not about making numbers look large. Practitioners must connect generation, sellable energy, demand patterns, site conditions, and long-term risks so that post-installation reality does not diverge greatly from the explanations given.

# Using feed-in revenue simulations to inform design decisions

The purpose of reading feed-in revenue from solar generation simulations is not only to forecast income but to inform design decisions. Careful interpretation of feed-in revenue helps examine system capacity, panel layout, orientation, tilt, combinations with storage, prioritization of self-consumption, and operational policies. Thus, interpreting feed-in revenue becomes material for judging the quality of a design.

When considering system capacity, maximizing generation is not always optimal. Filling every available surface with panels tends to increase annual generation, but it can also produce excessive surplus or create times when exports cannot be made. Insufficient inspection space may hinder long-term maintenance. When reading feed-in revenue, evaluate whether the design leads to manageable operation and stable revenue rather than focusing solely on maximum generation.

Panel layout decisions also benefit from interpreting feed-in revenue. Forcing panels into areas prone to shading may increase simulated capacity but limit actual generation and sales. Avoiding shaded portions of the roof may improve overall efficiency. Rather than aiming for total generation, aim for low-loss generation to improve feed-in revenue stability.

Feed-in revenue estimates also inform the balance with self-consumption. Facilities with significant daytime demand may be better served by designs that prioritize self-consumption. Conversely, facilities with stable surplus may benefit from designs assuming sales. Which to prioritize depends on facility use, operating hours, and future plans. Simulations enable comparison of multiple design options to determine which best meets objectives.

Accurate site data are critical to using feed-in revenue simulations for design decisions. If roof and site shapes, orientations, elevation differences, and surrounding obstructions are accurately known, generation estimates become more realistic. Conversely, designing with ambiguous site conditions leads to uncertain feed-in revenue estimates. For large sites, complex roof shapes, or facilities with many surrounding structures, the precision of position data and current condition awareness strongly affect simulation results.

To increase the precision of such site understanding, iPhone-mounted GNSS high-precision positioning devices like LRTK are an effective option. High-precision records of roof surroundings, site boundaries, obstructions, and equipment positions made on site make it easier to align simulation assumptions with reality. To read feed-in revenue accurately, it is essential to capture site shapes and positions correctly, not just perform desk calculations. Using LRTK helps streamline pre-design site surveys, identification of generation loss factors, and information sharing for subsequent steps.

# Summary

When reading feed-in revenue from solar power generation simulations, do not judge solely by the size of annual generation. Generation and export are not identical; you must separate how much of the generated energy is self-consumed and how much can be sold as surplus. Correct interpretation of feed-in revenue requires checking sellable energy, monthly surplus energy, self-consumption rate versus export ratio, conditions that lead to overestimation, and corrections based on site conditions.

In particular, practitioners should not accept simulation results as revenue forecasts without checking assumptions and comparing them with site reality. Solar irradiation, shading, soiling, aging, equipment downtime, demand patterns, and connection conditions all affect feed-in revenue. Considering the potential for downside as well as favorable conditions increases the credibility of internal briefings and client proposals.

The ability to read feed-in revenue is not just about calculating income. It is the ability to link generation, electricity demand, system design, and site conditions to make decisions that reflect post-installation reality. When using solar generation simulations, avoid relying too much on surface numbers and carefully confirm the basis for sellable energy.

Finally, improving the accuracy of feed-in revenue interpretation requires precise site position and shape data. Accurately capturing roofs, sites, surrounding obstructions, and equipment positions aligns simulation assumptions with reality and brings feed-in revenue estimates closer to actual performance. If you want to proceed with solar design and site surveys efficiently, using LRTK—an iPhone-attachable high-precision positioning system—can improve the accuracy and speed from site verification to design documentation.