Study the REP material by building a two-column list of confusable terms, then drilling each pair through a short calculation until you can state when each term applies. Work full mini-cases rather than isolated definitions.
Capacity Factor vs. Efficiency: Stop Comparing Them as Quality Scores
Capacity factor measures actual output against nameplate potential over time; efficiency measures conversion of an input into useful output. A low capacity factor is normal for solar, while efficiency describes the equipment itself.
Capacity factor is actual annual energy divided by the energy a system would produce running at nameplate power for all 8,760 hours of the year. Solar in most regions lands well below 25% because of nights, clouds, and seasonal variation, while wind turbines at strong sites often run higher. Efficiency means something different: a PV module converting sunlight to electricity, or a rotor extracting kinetic energy from wind. The two numbers respond to different variables, so comparing them across technologies cannot rank which technology is better.
Apply them separately in exam-style scenarios. If a question compares a 20%-efficient panel with a 22%-efficient panel at identical irradiance, efficiency decides. If it compares a solar array with a wind turbine, resource data and capacity factors decide, and efficiency is largely irrelevant to that comparison. When you sanity-check a production estimate, capacity factor is the tool: annual output divided by nameplate times 8,760 should land in a plausible band for that technology and resource, and an implausible figure signals a sizing or unit error somewhere in your work.
Power vs. Energy: Keeping kW and kWh Straight in Load Analysis
Power in kilowatts is an instantaneous rate; energy in kilowatt-hours is power sustained over time. Sizing decisions use both: system output in kW against peak demand, and annual kWh against total consumption.
A 100 kW system running at full output for one hour delivers 100 kWh; the same system at half output for two hours also delivers 100 kWh. Load profiles show both dimensions: demand peaks measured in kW and total consumption measured in kWh. A renewable system offsets consumption whenever it produces, but it reduces demand only when its output coincides with the facility's peak. These are different value streams, and mixing them up changes the savings calculation.
When a scenario hands you a facility's annual kWh and its peak kW, first decide which one the question targets. Offsetting consumption feeds directly into energy-cost savings math. Claiming demand reduction requires the generation profile to line up with the peak, which solar frequently does not for evening peaks. In written answers, state the coincidence assumption explicitly rather than silently assuming generation matches load. This single habit prevents the most common unit-level errors in assessment questions.
Scenario 1: Sizing PV Without the Classic Derate Mistake
Estimate PV annual energy as nameplate kW times peak sun hours times a performance ratio times 365. The common slip is multiplying nameplate by sun hours alone and ignoring real-world losses.
Scenario: a facility wants to offset 14,600 kWh per year, and the site averages 5.0 peak sun hours per day. The tempting shortcut is to compute kWh per installed kW as 5.0 times 365, giving 1,825 kWh per kW, and size an 8 kW array. That math assumes every rated watt reaches the meter. In reality soiling, temperature, inverter and wiring losses, and downtime reduce delivered energy, which is why a performance ratio is applied. Treating the panel rating as delivered output quietly overstates production by the full loss fraction.
The better decision applies the performance ratio of 0.80: expected yield is 5.0 x 0.80 x 365 = 1,460 kWh per kW, so the target requires 14,600 / 1,460 = 10 kW. The shortcut leaves the customer about 20% short of the stated goal, which is a credibility problem, not just a math problem. Note that 0.80 is a scenario assumption, not a universal constant; different sites and system designs produce different ratios. In your answer, name the losses the ratio bundles and flag that the value is given, not invented.
Wind Feasibility: Why a Small Wind-Speed Change Changes Everything
Available wind power scales with the cube of wind speed, so feasibility analysis must use hub-height wind distribution and the turbine's power curve, not a regional average speed.
Because power in the wind rises with the cube of velocity, a move from 5 m/s to 6 m/s, only a 20% increase, raises available power by roughly 73%. Air density and swept area also matter, and the Betz limit caps the fraction of that power any rotor can extract. A specific machine's actual output follows its power curve, which rises to a rated wind speed, flattens, and stops at the cut-out speed. The curve, not the cube law alone, predicts real production.
In scenarios, this means a town's average wind speed of 5 m/s does not establish project feasibility. The distribution of speeds, hub height, terrain, and turbulence all shape output, and two turbines with the same nameplate can differ substantially at one site. Compare candidate machines by expected energy at the site's speed distribution rather than by rating. If the scenario supplies insufficient wind data, the defensible professional answer is to request hub-height measurement or validated long-term data instead of committing to a capacity estimate.
Scenario 2: Payback vs. Lifecycle Metrics When Comparing Two Options
Simple payback ignores everything after the recovery point and the project's life; LCOE, NPV, and IRR capture lifetime performance. Match the metric to the decision the scenario actually asks for.
Scenario: Option A costs $40,000, saves $10,000 per year, and lasts 8 years; Option B costs $55,000, saves $9,000 per year, and lasts 25 years. On simple payback, A wins at 4 years versus roughly 6.1 years for B. But over each system's own life, A delivers $80,000 in gross savings minus its cost, about $40,000 net, while B delivers $225,000 minus $55,000, about $170,000 net before discounting. Choosing A on payback alone discards more than half the achievable net benefit and locks in a replacement around year 8.
The better decision asks what the scenario optimizes. If capital is tightly constrained and near-term cash recovery is the goal, payback is a legitimate screen, and A is defensible. If the question compares lifetime value or asks which option better serves a long planning horizon, use NPV with the given discount rate, or compare net lifetime savings as an approximation. State which metric you used and why. See the table below for where each metric misleads.
| Metric | What it captures | Where it misleads |
|---|---|---|
| Simple payback | Time to recover the initial cost from annual savings | Ignores savings after payback, project life, and time value of money |
| LCOE | Average cost per kWh generated over the system life | Says nothing about the timing or size of cash savings at the host facility |
| NPV | Lifetime net value expressed in today's dollars | Highly sensitive to the discount rate you assume |
| IRR | Percentage return on the invested capital | Ambiguous when cash flows change sign more than once |
Net Metering, RECs, and Interconnection: How Policy Changes the Math
Export compensation, renewable energy certificates, and interconnection limits determine whether exported energy is worth retail or avoided-cost rates and whether oversizing a system is even permitted.
Net metering rules, which vary by jurisdiction, decide how exported energy is credited, whether at retail rates or a lower avoided-cost value. Renewable energy certificates are separate environmental attributes that may be retained by the owner or transferred to the utility or another buyer. These mechanisms change the effective value of each exported kilowatt-hour and can flip a comparison between a design that maximizes self-consumption and one that exports heavily. A savings calculation that ignores how exports are compensated is incomplete regardless of how accurately the production was estimated.
Interconnection and host-load considerations also constrain sizing: a utility may cap system capacity, and output well above on-site load may earn little under certain compensation rules. On the professional-standards side, document which programs and rates your analysis assumes, avoid double counting by claiming both energy savings and the attached RECs as separate wins to different audiences, and flag that incentive programs change over time. A defensible report states its jurisdiction and program assumptions instead of quoting a generic percentage that may not apply.
Practice Exercise, Self-Check Rubric, and a Two-Week Drill Sequence
Work one full mini-case per week: estimate output, choose a financial metric, and write out assumptions. Grade yourself against a rubric rather than a raw score to locate the weak concept.
Exercise: an office consumes 60,000 kWh per year; its roof fits a 15 kW PV array at a site averaging 4.8 peak sun hours per day, with a performance ratio of 0.78. The system costs $2.20 per watt, the retail rate is $0.14 per kWh, and all output is self-consumed. Compute annual output (15 x 4.8 x 0.78 x 365 = 20,498 kWh, about 20,500), annual savings (about $2,870), simple payback (about 11.5 years), and implied capacity factor (20,498 / 131,400, about 15.6%). Expected observations: output near 20,500 kWh, payback in the low teens, capacity factor in the mid-teens, and the offset covers roughly a third of consumption.
Grade your written work against this rubric: units correct at every step; the loss factor applied exactly once, not twice; the financial metric matched to the question; every assumption stated in one line; and conclusions limited strictly to the given data. An adaptable sequence: first pass, build the paired-concept list and drill the two calculations above until the method is automatic; second pass, write two timed mini-cases per week and grade with the rubric; final pass, rework every case you missed and confirm you can state three assumptions unprompted. These are learning milestones for self-assessment, not predictions of any score.
- Readiness check 1: you can compute PV annual output with a performance ratio without looking up the method.
- Readiness check 2: you can explain in two sentences why an average wind speed is not a feasibility verdict.
- Readiness check 3: given three project comparisons, you can name the right financial metric for each and say why.
- Readiness check 4: your written case answers list assumptions before conclusions, with no unit mismatches.
- For current eligibility, exam format, and renewal rules, rely on AEE's own certification pages rather than secondary summaries.
References and further reading
Use these references to explore the concepts and check the latest information from the relevant organizations.