6 Tips to Avoid Failures in Solar Power Generation Simulations

Solar power generation simulations are an important basis for decision-making in power plant planning, design, financial assessments, internal approvals and stakeholder briefings, and post-construction generation evaluations. However, simulation results change depending on input conditions and how assumptions are set. Even for the same site and the same installed capacity, changes in solar irradiance, shading, orientation, tilt angle, losses, degradation rate, and expected operating conditions will alter the projected annual generation and financial outlook.

Especially in practice, when decisions are made based solely on the numerical results of calculations, risks that were hard to see during the planning stage can later surface. Problems such as lower-than-expected power generation, financial results not proceeding as planned, difficulty explaining things to stakeholders, and large discrepancies between post-construction performance and simulations can arise from insufficient verification of initial conditions or inadequate sharing of assumptions.

This article summarizes six points that practitioners should check to prevent failures in solar power generation simulations. The purpose is not merely to produce figures for power output, but to enable judgments about what assumptions those numbers are derived from, where uncertainties lie, and to what extent they can be used for decision-making.

Table of Contents

• Do not leave the purpose of the simulation ambiguous

• Ensure input conditions match the actual plan

• Do not over-rely on solar irradiance and weather conditions

• Do not underestimate the effects of shading and the surrounding environment

• Do not arbitrarily assume loss conditions and degradation rates

• Check not only the numerical results but also the assumptions and the differences

• Summary

Do not leave the purpose of the simulation ambiguous

The first thing to be careful about in solar power generation simulations is not to leave ambiguous what the simulation is for. If the only objective is to know the amount of power generated, it can be difficult to set conditions that are usable in practice. Whether it is a new power plant plan, consideration of expanding existing equipment, a profitability forecast, pre-construction design verification, or a comparison with actual generation results, the required level of accuracy and the items to be checked will vary.

For example, at the planning stage of a new solar power plant, it is necessary to check not only the annual power generation but also monthly generation trends, seasonal variations in generation, the impact of shading, the appropriateness of output relative to installed capacity, and projections that include future degradation. On the other hand, when investigating why an existing facility’s power generation is lower than expected, it is important to sort out the actual-side conditions, such as differences between planned and actual values, weather factors, downtime, soiling, shading, equipment malfunctions, and output curtailment.

If you proceed with simulations while the objective is unclear, you may end up with neatly presented power generation figures that are difficult to use for decision-making. Whether the analysis is for internal review, customer presentations, explanations to financial institutions, or construction decisions will change the level of explanation required. For documents that affect profitability in particular, it is important not to present only optimistic generation estimates, but to separate standard, conservative, and worsened-condition estimates.

Also, simulations do not perfectly predict future power generation. They are forecasts based on specific assumptions. Therefore, you should decide in advance what the figures are meant to assess, depending on your purpose. Whether you want to evaluate the appropriateness of system capacity, the effect of a layout change, the annual amount of electricity sold, or the amount of self-consumption will change both the input conditions required and the output items you need to check.

In practice, before starting a simulation, organizing the scope, the items to be compared, and the evaluation criteria can make it easier to avoid failure. By "scope" we mean whether the target is the entire power plant, a specific section, or each roof surface. By "items to be compared" we mean comparisons of multiple layout proposals, tilt angles, orientations, system capacities, loss conditions, and so on. By "evaluation criteria" we mean annual energy production, monthly generation, capacity factor, financial performance, payback, risk tolerance, and other similar factors.

If you try to calculate power generation without this kind of preparation, you'll later have to verify things like "Are these conditions different from the actual site?", "Can this figure be used in the financial calculations?", and "Does this include shading effects?". As a result, recalculations and redrafting of explanatory materials become necessary, leading to rework in the review process. For simulations, setting the initial objectives and clarifying the assumptions is more important than the calculation work itself.

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Ensure the input conditions match the actual plan

The results of solar power generation simulations depend heavily on the accuracy of the input conditions. Not only solar irradiance and weather conditions, but also installation location, orientation, tilt angle, system capacity, wiring configuration, converter capacity, panel layout, racking height, surrounding obstacles, operating conditions, and various other information influence the outcome. If the input conditions deviate from the actual plan, no matter how detailed the calculations, the prediction may end up far from reality.

What requires particular attention is the situation where the rough assumptions made during initial studies continue to be used in detailed analyses. In the early planning stages, it is common to estimate power generation using approximate equipment capacities and provisional layouts. At this stage, speed is important, so making certain assumptions is not in itself a problem. However, if the layout, the number of panels, the tilt angle, or other factors change later, yet simulation values from the old conditions are still used, misunderstandings can arise in profitability assessments and when explaining the project to stakeholders.

Entering the installation location is also important. Solar power generation is affected by local solar irradiation conditions, so if the location setting is too coarse, the expected power output can differ. Rather than setting only a broad region name, it is desirable to set the location to a point as close to the actual site as possible. Also, in mountainous areas, coastal areas, regions with heavy snowfall, areas prone to fog, or sites surrounded by tall buildings or trees, general regional conditions alone may not adequately reflect the actual situation.

Regarding system capacity, it is insufficient to judge it solely by the simple sum of panel capacities. The amount of electricity that can actually be extracted varies depending on DC-side capacity, AC-side capacity, power conversion equipment capacity, circuit configuration, how oversizing is handled, and whether there are output limitations. In generation simulations, it is necessary to check not only the equipment's nominal values but also whether the settings reflect actual operating conditions.

Orientation and tilt angle affect seasonal variations in power generation and output by time of day. In Japan, configurations that are closer to south-facing generally tend to secure higher annual generation, but when roof installations or site constraints apply, systems may be oriented east–west or distributed across multiple surfaces. In such cases, representing the system by the conditions of a single surface can lead to discrepancies with the actual generation curve. When there are multiple roof planes or multiple orientations, it is important to separate and verify the conditions for each plane.

Also, it is necessary to verify that the conditions shown on the drawings match the actual site. Drawings may not reflect obstacles; on site, neighboring buildings or trees may cast shadows; parts of the planned installation area may be unusable; and construction clearances may be required. Performing a simulation without accounting for these conditions can easily lead to overestimated planned values.

To ensure that input conditions match the actual plan, it is useful to create a checklist of conditions before performing calculations and cross-check it against drawings, on-site information, equipment specifications, and construction conditions. You should also document the rationale for input values so that anyone can verify them under the same assumptions. If the layout or equipment configuration changes later, update the simulation and implement version control so that old results are not confused with the new ones.

Don't Overestimate Solar Radiation and Weather Conditions

In solar power generation simulations, solar irradiance and weather conditions are important baseline data. Because solar power uses energy from the sun, estimates of solar irradiance strongly affect forecasts of electricity generation. However, it should be noted that solar irradiance data do not guarantee future weather. Even when calculations are made using past statistics or standard meteorological data, generation output will vary due to year-to-year weather differences.

In some years there are many clear days, and actual output can exceed the simulated values. Conversely, in years with prolonged rain, typhoons, heavy snowfall, or many cloudy days, power generation may be lower than expected. It is important to evaluate annual generation as a long-term trend; one should avoid concluding that the simulation is wrong based solely on a single year’s performance, and likewise avoid assuming that a strong single-year performance means the same will occur in the future.

Weather conditions involve not only solar irradiance but also air temperature. Solar cells generally tend to lose output as temperature rises, so in summer even with high irradiance there are losses due to temperature increases. The installation method also affects heat dissipation. When installed close to the roof, temperatures can rise more easily than for ground-mounted installations. Conversely, under well-ventilated installation conditions temperature increases may be reduced.

In snowy regions, it is necessary to consider not only statistics of solar radiation but also temporary power generation stoppages caused by snow, the effects of reflection, whether snow removal is performed, and how easily snow slides off due to panel tilt. When the panel surface becomes covered after snowfall, there will be periods during which no power can be generated even if it is sunny. If the handling of snow is not sufficiently reflected in simulations, winter power generation may not match actual results.

Care should also be taken in coastal and mountainous areas. In coastal areas, salt, humidity, strong winds, and the deposition of dirt can affect the condition of equipment. In mountainous areas, shadows in the morning and evening, fog, and differences in solar radiation caused by topography are more likely to occur. Even within the same municipality, sunlight and shading conditions can differ between flatlands and mountain slopes. Therefore, relying solely on wide-area average data can overlook site-specific conditions.

When using solar radiation data, it is important to check what period the statistics are based on, which location the information refers to, and what level of spatial accuracy it has. Also, if you can compare multiple meteorological datasets, looking at differences in power generation makes it easier to grasp the range of predictions. Rather than treating a single result as an absolute value, understanding how much it can vary depending on conditions is the key to preventing failures.

In cash-flow analyses, it is essential to incorporate uncertainty in solar irradiance. You need to verify not only the standard expected power generation but also whether the project remains viable if generation turns out somewhat lower. This is especially important when loans or long-term contracts are involved, because a downside deviation in generation can affect the finances. Simulation values are a useful benchmark, but they should be treated on the assumption that weather conditions vary.

Don't underestimate the impact of shadows and the surrounding environment

A commonly overlooked factor in solar power generation simulations is the effect of shading and the surrounding environment. Power generation is not determined solely by solar irradiance. When shadows fall on the panel surface, output during that period decreases. Even if the extent and duration of shading are limited, depending on the circuit configuration and how the shading occurs, it can have a non-negligible impact on power generation.

Causes of shading include buildings, trees, utility poles, signs, fences, adjacent equipment, mountain ridgelines, roof-mounted protrusions, air-conditioning equipment, handrails, chimneys, and so on. In ground-mounted power plants, inter-row shading, where the front row of panels casts shadows on the rear row, is also important. Especially in winter, when the sun’s altitude is lower, obstacles that are not problematic in summer can cast shadows in the mornings and evenings.

For roof-mounted installations, shading can be caused not only by surrounding buildings but also by the roof’s shape, level differences, rooftop structures (penthouses), equipment foundations, and lightning protection equipment. When installing across multiple roof surfaces, each surface receives different sunlight, so simply adding up capacities can make it difficult to estimate actual power generation. Treating heavily shaded surfaces and lightly shaded surfaces under the same conditions can lead to overestimation or underestimation of expected generation.

In shadow simulations, it is important to check the relationship between the sun’s movement throughout the year and the positions of obstacles. Visiting the site once during the daytime is not enough to grasp seasonal changes in shadows. A location that appears fine in summer can produce long shadows in winter. Even if shadows only occur in the morning and evening, it is necessary to verify how much they will affect power generation.

Regarding trees, you should consider not only their current height but also future growth and whether they will be pruned. Even if the impact is small at the planning stage, branches and foliage can grow over a few years and increase shading. In addition, soiling from the surrounding environment—such as fallen leaves, bird damage, dust, and dirt from nearby farmland or factories—can also affect power generation. Simulations sometimes assume a certain percentage as soiling loss, but in some site environments the standard settings may be insufficient.

The influence of topography is also important. In mountainous areas and valley terrain, the morning and evening sun can be blocked, shortening the hours of sunlight. Simulations that do not take surrounding terrain into account can overestimate power generation compared with reality. Especially in locations where shadows are likely during periods of low solar altitude, checking monthly and hourly generation curves makes it easier to grasp the impact of shading.

To avoid underestimating shadows, it is important to combine on-site inspections, drawing reviews, photographic records, and an inventory of surrounding obstructions. As needed, check not only whether shadows occur but also in which seasons and at approximately what times of day, and over what areas the shadows will fall. By clarifying whether shadow conditions are reflected in the simulation results—and if not, what level of risk should be expected—you will make it easier to explain any subsequent differences in power generation.

Do not arbitrarily assume loss conditions and degradation rates

In photovoltaic power generation simulations, various losses are taken into account for the expected power output derived from solar irradiance. The annual energy yield varies depending on how those loss assumptions are set. If only favorable conditions are assumed, the projected values can become overstated, which may lead to incorrect assessments of profitability.

Major losses include output reduction due to temperature, wiring losses, conversion losses, soiling losses, shading losses, losses from equipment downtime, aging degradation, output curtailment, and inter-circuit variability. In actual power plants, these occur in combination. Each may seem small, but when they accumulate, they can create differences in generated power that cannot be ignored.

Temperature loss refers to the reduction in output caused by a rise in the panel’s temperature. In hot regions or under poorly ventilated installation conditions, temperature-related losses tend to be larger. Wiring losses vary depending on cable length, thickness, and circuit design. Conversion losses occur in the process of converting direct current power to alternating current. Even when these losses are treated uniformly as standard values, it is necessary to verify whether those values are appropriate for the specifics of the plan.

Soiling losses cannot be ignored. When sand and dust, pollen, yellow dust, bird droppings, fallen leaves, exhaust-related grime, and other contaminants adhere to the panel surface, power generation decreases. Rain may wash some of this away to a certain extent, but in low-tilt installations or environments where dirt easily accumulates, the impact can be greater than expected. Whether there is a cleaning plan or not, the long-term outlook for power generation will differ.

Downtime due to equipment outages and maintenance should also be considered in financial assessments. Inspections, failures, communication problems, operation of protective devices, or grid-side constraints can cause periods when generation is not possible. Since simulations tend to assume equipment operates normally throughout the year, in practice it is necessary to separately verify availability and outage risks.

Setting the degradation rate is also important. Because photovoltaic systems are operated over long periods, it is necessary to anticipate not only first‑year generation but also the decline in generation after several years and after a decade or more. If the degradation rate is set too low, long‑term financial performance will look better, but if it does not match the actual condition of the equipment or environmental conditions, future returns will be overestimated. Conversely, if it is set overly conservatively, the project’s viability will be underestimated more than necessary.

It is important not to exclude the possibility of output limitations or curtailment from consideration. Depending on grid conditions, contractual terms, and local operational practices, it may be necessary to reduce output even when generation is possible. If you do not clarify whether to include this in the generation simulation or to account for it separately in the financial/profit-and-loss calculations, projected feed-in volumes and self-consumption estimates may not match reality.

Loss assumptions and degradation rates can produce more attractive results if you make the numbers smaller. However, the purpose of a simulation is not to produce attractive numbers but to create decision-making information that is close to reality. Plans that underestimate losses tend to show large discrepancies with actual performance later and become difficult to explain to stakeholders. It is important to separate and organize standard conditions, conservative conditions, and site-specific conditions, and to make clear under which assumptions the estimated energy output was calculated.

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Check not only the resulting numbers but also the assumptions and the differences

In solar power generation simulations, attention tends to focus on the annual generation figures. However, to avoid failures in practice, it is essential to check not only the numerical results but also the assumptions behind them and any differences. If you judge solely by whether the annual generation is large or small, it becomes difficult to understand why that result occurred and which conditions are affecting the generation.

First, what you should check is the monthly power generation. Even if the annual generation is the same, differing monthly generation balances will change the economics and the approach to electricity use. In plans focused on selling power, the annual total tends to be emphasized, but when prioritizing self-consumption it is important that the times of day and seasons of generation align with electricity demand. You need to confirm whether generation is higher in summer, how much it drops in winter, and how much impact the rainy season or snow season is expected to have.

Next, we assess the reasonableness of the generation relative to the installed capacity. Even with the same installed capacity, annual generation varies depending on the region, orientation, tilt, shading, and loss conditions. When comparison with past similar projects or the performance of nearby installations is possible, check whether it is not excessively high or low. However, rather than judging good or bad by simple comparisons alone, it is important to clarify the differences in conditions. If the installation angle, shading, snowfall, overloading, or operating conditions differ, generation will also differ.

Also, when comparing multiple simulation scenarios, clearly state which conditions were changed. Even if you think you are comparing layout options, if equipment capacity or loss conditions have also been altered, the reasons for differences in power generation will become unclear. When making comparisons, separate the conditions that are changed from those that are kept fixed, and present them in a way that allows you to explain the causes of the differences.

When using the resulting figures to calculate profitability, also confirm the assumptions used to convert generation output into revenue and savings. Because conditions such as the feed-in tariff and electricity consumption vary by project, this article does not discuss specific prices; however, generation output and profitability are not the same thing. Even if generation output is large, if it does not match the times when it can be used, the benefit from self-consumption may be limited. Conversely, even if the annual generation output is not particularly large, if it aligns with demand the benefits are more likely to materialize.

When comparing measured performance with simulations, it is important to compare them under the same conditions. Actual results reflect weather, downtime, output limitations, cleaning status, equipment malfunctions, and so on. If the simulation was calculated under standard meteorological conditions, simply comparing annual totals will not reveal the cause. Even if actual performance is low, you need to distinguish whether it is due to unfavorable weather, shading or soiling, or equipment outages.

To verify assumptions and differences, it is useful not only to save the simulation results by themselves but also to record the input conditions, calculation date, version of the drawings used, equipment configuration, loss settings, the treatment of shading, degradation rate, and the responsible person's judgment notes. If conditions change later and it becomes unclear which version of the simulation is being viewed, misunderstandings can arise among stakeholders. Version control is especially important for projects where multiple options are being considered in parallel.

Simulation results are not a single answer but conditional material for judgment. When interpreting the results, you need to check annual generation, monthly generation, the breakdown of losses, the effects of shading, the relationship with installed capacity, and the impact on financial performance. Being able to explain the assumptions from which the numbers arise, not just the magnitude of the numbers, is what makes a simulation usable in practice.

Summary

To prevent failures in solar power generation simulations, it is important not to focus only on the numerical results, but to check each item—purpose, input conditions, meteorological conditions, shading, losses, degradation rate, and how to interpret the results. Simulations are a useful tool for predicting power generation, but they do not fully guarantee future generation. They should be treated, under certain assumptions, as material for judging plans and financial projections.

The first important thing is to clarify the purpose of the simulation. Whether you are evaluating a new project, checking profitability, or analyzing the performance of existing equipment will change the conditions that need to be set. If the purpose remains vague, even if you obtain power generation figures, you will not know which decisions to base those figures on.

Next, you must match the input conditions to the actual plan. If equipment capacity, orientation, tilt, layout, circuit configuration, installation location, surrounding obstructions, or other factors differ from the actual situation, the results will also deviate from reality. If the plan changes, be careful not to continue using simulations based on the old conditions.

Do not be overconfident about solar irradiance and meteorological conditions. Standard meteorological data are useful, but actual performance will vary due to year-to-year weather differences, temperature, snowfall, and site-specific environmental factors. When conducting a profitability assessment, you should check not only the standard projections but also the impact if power generation falls short of expectations.

Checking shadows and the surrounding environment is also essential. Buildings, trees, terrain, rooftop equipment, and inter-row shading can affect power output. It is important to combine on-site inspections with drawing reviews to understand how shading changes with the seasons and time of day.

Loss conditions and degradation rates greatly affect how a simulation appears. If temperature losses, wiring losses, conversion losses, soiling, downtime, aging degradation, output limits, and similar factors are conveniently underestimated, the planned values will be overestimated. To create a plan that is usable in the long term, appropriate settings that match the site conditions are necessary.

Finally, it is important to check not only the resulting numbers but also the assumptions and differences. By looking not only at annual generation but also at monthly generation, the breakdown of losses, the effects of shading, the relationship with installed capacity, and differences from actual performance, you can more easily understand a plan’s strengths and risks. When explaining simulation results to stakeholders, having the assumptions well organized also makes it easier to demonstrate the basis for the figures.

Solar power generation simulation is a critical process that can determine the success or failure of a power plant project. To make decisions that are practical for field use, it is essential to carefully reflect local site conditions, not rely solely on optimistic figures, and interpret results from multiple perspectives. If you want to manage consistently from energy output forecasting, organizing design conditions, and financial assessments to post-operation performance verification, organize site information, design drawings, equipment conditions, and simulation results under the same assumptions, and utilize experts and design-support tools as needed.