Prepare for the REP exam by drilling concept discrimination rather than fact recall. For every topic, ask what decision the concept supports, contrast it with the adjacent concept, and practice applying it to a one-page project scenario with explicit assumptions. Work through the scenarios, table, exercise, and readiness checks below, and treat any self-check scores as learning milestones, not predictions.
Nameplate capacity, capacity factor, and delivered energy are not interchangeable
Nameplate capacity is a rated maximum under standard conditions. Capacity factor is average output divided by rated output over a period. Delivered energy is the actual kWh produced. Each answers a different project question, and quoting the wrong one corrupts the decision.
A worked example ties the three together: a 100 kW PV array running at an 18% capacity factor delivers roughly 100 x 8,760 x 0.18, about 157,700 kWh per year. Nameplate describes what the system can do at rated maximum; capacity factor describes what it actually does over time at that site; delivered energy in kWh is what the customer uses or sells. All three describe the same system, yet only one belongs in any given sentence.
The practical skill is matching quantity to question. Sizing electrical infrastructure and comparing technology scale call for nameplate ratings. Screening sites or comparing output expectations across technologies calls for capacity factor. Billing savings, emissions reductions, and payback calculations need delivered energy. A scenario that quotes a 100 kW system's 'savings' without converting through capacity factor and site conditions has a rated maximum masquerading as annual production, and every downstream number inherits the error.
Practice the discrimination by taking any practice question and labeling which quantity it targets before computing anything. Write the three terms at the top of your scratch work and force yourself to circle one. This habit takes seconds and prevents the specific slip where a rated maximum gets quoted as an annual production figure.
Solar resource data: GHI, DNI, and plane-of-array irradiance answer different questions
GHI is measured on a horizontal surface, DNI is direct beam on a sun-tracking surface, and POA is irradiance striking the tilted collector. Applying GHI directly to a tilted array misstates the resource, typically understating it before any losses are applied.
Scenario: a facility manager compares two proposals for a 500 kW fixed-tilt rooftop array. Vendor A estimates production from local GHI of about 1,700 kWh per square meter per year applied directly to the array, skipping both transposition and derates. Vendor B transposes GHI to POA for the stated tilt and orientation, then applies shading and soiling losses. Vendor A's number happens to come out higher, but the reason matters: at mid-latitude tilts, transposition to POA typically adds a tilt gain, while omitted derates cut production. A naive GHI figure skips both adjustments, and the errors can push it either way.
The better decision is Vendor B's method, because only POA reflects where the modules actually point and only explicit derates make the estimate auditable. Array sizing, inverter selection, and financial projections all inherit whatever the resource step produced. Learn the three measures as a decision map: GHI suits quick regional comparisons and horizontal installations; DNI drives concentrating solar and tracking systems; POA is the input for fixed-tilt production modeling, built from GHI by geometric transposition.
Before using any resource number, check whether the stated surface orientation matches the collector orientation. Annotate every irradiance figure in your notes with its surface: horizontal, tracking, or tilted at a stated angle. A quick self-check: write one sentence for each term naming the surface it is measured on. If you cannot do this from memory, reread the definitions and repeat tomorrow; fluency here sits underneath production, economics, and technology comparisons.
Wind assessment: average wind speed alone hides the distribution that sets output
Available wind power scales roughly with the cube of wind speed, and turbine output follows its power curve. Two sites with the same average speed can differ substantially in energy, and which one wins depends on where the mean sits relative to rated speed.
The cubic relationship has a blunt implication: small errors in wind speed estimates produce large errors in energy estimates. Work a labeled hypothetical under a simplified cube assumption: doubling wind speed multiplies available power by eight, not two. Real output also caps at the turbine's rated speed, so hours above rated contribute no extra energy. Wind shear matters too, because speed generally increases with height; hub height determines the resource the rotor actually sees. When a scenario reports wind data, check the measurement height and whether a distribution or a single average is given.
Now compare two hypothetical sites both averaging 6 meters per second: Site X blows steadily near 6 m/s, while Site Y alternates between near-calm and strong gusts capped at rated speed. At a 6 m/s mean, well below a typical rated speed, most hours sit on the convex, steep part of the cubic curve, so the variable site actually averages more power than the steady one. Steadiness wins when the mean sits near rated speed, where variability only wastes energy above the cap. In exam scenarios, resist choosing a site on the average alone; the direction of the distribution effect depends on the curve's shape.
When reviewing any wind question, list four items before answering: measurement height, hub height, whether the figure is an average or a distribution, and which region of the power curve the speeds occupy. This short checklist converts a vague resource question into a structured evaluation you can defend in writing.
Comparing renewable options: match the economic metric to the decision
Simple payback ignores the time value of money and everything after payback; LCOE normalizes lifetime cost per unit of energy; NPV and IRR capture cash flow timing and scale. Screening, financing, and ranking decisions each favor a different metric.
LCOE divides lifetime cost of building and operating a system by its lifetime energy output, giving a cost per kWh for rough cross-technology comparison. Simple payback divides upfront cost by annual savings and is easy to communicate but blind to project life, degradation, and discount rates. NPV states absolute value created at a given discount rate; IRR expresses return rate but can mislead across very different project scales. A scenario asking you to 'compare two technologies' is really asking which metric the decision requires.
Scenario: a plant manager chooses between a solar PV project with high upfront cost, low operating cost, and long life, and a biomass heat project with lower upfront cost but ongoing fuel costs and more O&M attention. Ranking by simple payback and calling the result an economic comparison is the mistake to avoid; it rewards the cheap project's early cash flows and ignores its shorter life and fuel exposure. The better decision: use NPV at the organization's discount rate for board-level ranking, LCOE for a unit-cost sanity check, and payback only as a communication device for leadership that thinks in those terms.
Use the table below as a drill: cover the right-hand columns and reconstruct each entry from the concept definitions. The goal is not memorizing rows but internalizing which question each row answers, so a scenario's wording tells you which tool to pick up.
| Metric | What it tells you | Best suited decision | Key blind spot |
|---|---|---|---|
| Simple payback | Years to recover upfront cost from annual savings | Quick communication of capital recovery | Ignores project life, discount rate, and post-payback cash flows |
| LCOE | Average lifetime cost per unit of energy produced | Screening and cross-technology unit-cost comparison | Hides cash flow timing and capital constraints |
| NPV | Absolute value created at a stated discount rate | Ranking projects and go/no-go investment decisions | Depends on the discount rate chosen |
| IRR | Rate of return implied by the cash flows | Characterizing return efficiency | Can mislead across very different project sizes and cash flow patterns |
| Capacity factor | Average output relative to rated output | Site quality and technology output comparison | Says nothing about cost, value, or timing of output |
Variable output and storage: size from the load shape, not the average
Solar and wind output varies with weather and time, so integration questions demand load shape analysis: when energy is needed, when it is produced, and what the peaks look like. Averages hide exactly the information that storage and demand-charge decisions require.
Scenario: a facility wants batteries to cut demand charges. An analyst sizes the battery from average daily kWh consumption and proposes a system that cannot discharge fast enough or long enough to shave the actual monthly peaks. The mistake is sizing from an average instead of a load duration analysis, which ranks interval demands from highest to lowest and shows how much load sits in the top few intervals. The better decision sizes power (kW) from the peak-shaving target and duration (hours) from how long those peaks persist, then verifies the battery can cycle that way within its limits. Demand charges respond to peak kW, not total kWh, so an average-based battery saves almost nothing on the bill.
Build the integration vocabulary around time. Net metering credits exported energy under defined rules, while other tariff structures compensate exports differently, so the value of surplus production depends on the applicable tariff, not physics alone. Curtailment means available renewable output cannot be used or exported and is wasted. Storage shifts energy in time and can provide power services, but each role imposes different sizing and cycling demands. Identify whether a scenario's driver is energy displacement, peak reduction, backup, or export value, because the same battery answers these four problems differently.
When a practice scenario mentions variability, immediately sketch or imagine the profile: a daily load curve, a solar production curve, and their overlap. Ask what fraction of load the renewable covers on an instantaneous basis versus an energy basis. The two numbers diverge sharply for variable generation, and noticing the divergence is the analytical habit these questions are designed to probe.
Documentation discipline: assumptions, baselines, and defensible feasibility claims
A renewable energy recommendation is only as strong as its documented assumptions and baseline. Practice writing an explicit assumptions register, defining the energy baseline before the project, and stating uncertainty, so conclusions can be checked and defended.
Professional practice separates the measurement baseline (energy use before the project, under stated conditions) from the assumed operating parameters that drive projections: resource data, degradation, availability, derates, escalation, and tariffs. A feasibility claim without an assumptions register cannot be reviewed, updated, or verified. Established measurement and verification frameworks formalize this: define the baseline, state adjustments for routine conditions, and specify how savings or production will be determined after installation. For any scenario conclusion, ask which assumptions it rests on and which a reviewer would challenge first.
Practical exercise: build a synthetic case you control. Invent a facility with a stated annual electricity consumption, a simple load profile, a local capacity factor for rooftop solar, and a stated tariff. Write a one-page feasibility memo recommending or rejecting a PV project. Include an assumptions register with at least six entries, the baseline energy figure, a production estimate traced through the capacity factor conversion, and one sensitivity line showing how the recommendation changes if the capacity factor is 20% lower. Expected observations when you self-check: the production figure traces arithmetically from the assumptions; every number in the memo appears in or derives from the register; and the sensitivity line either changes the recommendation or clearly states that it does not.
Self-check rubric, scored 0-2 each: (1) baseline defined with conditions stated; (2) resource data identified by measurement surface or source type; (3) production traceable through named conversion steps; (4) assumptions register complete and internally consistent; (5) sensitivity analysis present with an interpretation. A total of 8 or more indicates you are writing exam-ready reasoning; lower scores point to specific gaps rather than a predicted result. Repeat the exercise with a wind case, swapping the resource and capacity-factor assumptions.
An adaptable preparation sequence and concrete readiness checks
Structure preparation in three passes: concepts and definitions, scenario application, then timed mixed practice with self-scoring. Weight each pass by your background, and measure readiness with rubric performance rather than study hours.
A sequence you can adapt: in pass one, work through the concept pairs in this guide (nameplate versus capacity factor versus delivered energy; GHI versus DNI versus POA; LCOE versus payback versus NPV), writing one-sentence definitions and one decision each supports. In pass two, build three one-page scenarios from the section six template: a solar screening case, a wind comparison case, and a storage or demand-charge case, each with an assumptions register. In pass three, work mixed practice questions under time pressure, then rescore with the rubric. New to the field, weight pass one; working in it, weight passes two and three.
For administrative details such as eligibility, scheduling, and current credential requirements, consult the issuer directly; AEE's site at aeecenter.org is the authoritative reference and requirements can change. Treat the concepts here as the durable core and procedural details as belonging to the issuer's current pages. Keep your scenario library after pass three: rewriting one scenario per week keeps the discrimination skills sharp without a full restart. Free practice questions and broader study materials are available on this site to feed passes two and three.
Readiness checks before you consider yourself prepared: you can convert nameplate and capacity factor into annual energy in one line; you can name the correct irradiance measure for a fixed-tilt estimate and state its surface; you can explain why two sites with equal average wind speed can differ in energy, including which direction the distribution effect runs; you can state which economic metric a given decision requires and why; you can write a six-item assumptions register from memory; and your last three rubric scores were 8 or higher. Each check is a learning milestone, not a forecast of any particular exam outcome.
References and further reading
Use these references to explore the concepts and check the latest information from the relevant organizations.