Before designing any microgrid, you need one thing: clarity on demand.
A load study reveals how and when electricity is actually used â turning raw consumption data into actionable design intelligence.
Microgrid Feasibility & Economics
A load study analyzes electricity demand over time for a facility, campus, or communityâso your microgrid is sized for reality, not assumptions.
Instead of a single snapshot, a load study shows how power use shifts across hours, days, and seasonsârevealing peaks, patterns, and priorities that impact sizing and cost.
Ensure generation + storage can meet demandâespecially peaks and critical loads.
Connect energy use to CAPEX/OPEX and model payback and operating strategy.
Identify what must stay powered during outages and plan islanding duration.
Key Load Metrics Explained
The highest power demand over a period.
Why it matters: Sets required capacity for generators, inverters, and infrastructureâaverage demand can miss critical peaks.
Typical demand over time.
Why it matters: Helps estimate energy production needs, fuel use, battery cycling, and operating costs.
Total electricity used (kWh) over a period.
Why it matters: Informs annual cost, renewable sizing, and long-term performance modeling.
Average load á peak load.
Why it matters: Higher load factor = steadier demand and usually better economics; low load factor means sizing for infrequent peaks.
Shows how often different load levels occur.
Why it matters: Helps optimize generation + storage by revealing how frequent peaks really are.
Microgrid Basics
Not all loads are equal. When a microgrid runs in islanded operation, prioritization ensures limited power goes where it matters most.
Essential functions that must remain powered to protect life, safety, or core operations.
Loads that can be reduced or shut down during outages without severe consequences.
Loads that can be shifted in time or adjusted based on system conditions.
Load Study Inputs
Load studies typically rely on a combination of measured and estimated data. The quality and resolution of your inputs directly affect study accuracy.
Monthly energy use and peak demandâgreat for baseline understanding and early estimates.
Hourly or sub-hourly profilesâreveals the real peaks that drive equipment sizing.
Building/system-level visibilityâhelps pinpoint whatâs driving demand and prioritize loads.
Useful when metering is unavailableâestimate loads based on rated power and usage assumptions.
Best for new builds or expansionsâsimulate expected demand based on design and operations.
Bills are a strong startâbut interval and sub-metering reveal the peaks that make or break sizing.
Load Study Accuracy
The level of detail in load data matters. Resolution and study duration shape what you can seeâand what you might miss.
Hourly and sub-hourly data captures short-duration peaks that drive equipment sizing. Seasonal patterns reveal heating/cooling shifts. Multi-year data highlights trends and anomalies.
Load Profile Preview
This visual changes based on resolution
Captures short-duration peaks that often determine generator, inverter, and battery capacity.
Reflects heating/cooling swings and changing operational patterns across the year.
Reveals trends, anomalies, and growth signals that can impact long-term microgrid performance.
Feasibility Analysis
In microgrid feasibility studies, load data is the decision fuelâpowering technology choices, right-sizing, cost estimates, and resilience planning.
Feasibility models use the load profile to test configurations and operating strategiesâso designs work on paper and in the real world.
Outcome: decisions backed by dataânot guesswork.
Choose the right balance between generation and storage based on real demand behavior.
Size capacity to meet both peak demand and critical loadsâespecially during islanded operation.
Link energy use to equipment needs, cycling, and fuel inputs to forecast costs with confidence.
Determine how long critical loads can be supported during outagesâand what to shed first.
Common Pitfalls
Early-stage load studies often fail because of missing info, wrong assumptions, or poor forecasting. Below are the most common issuesâand what to do to fix them.
Problem
Teams use partial utility bills, outdated equipment lists, or mixed data sources.
Fix
Collect consistent data from the same time period (preferably 12 months), confirm equipment counts, and validate with walkthroughs or interval data.
â Do this:
Problem
Too many loads get labeled âcritical,â which increases system size and cost.
Fix
Separate loads into critical vs. non-critical and confirm what truly must stay on during outages.
â Do this:
Problem
The system is designed for today, not tomorrow, and becomes too small later.
Fix
Include realistic growth projections and expansion plans.
â Do this:
Problem
Averages hide real stress points, especially during peak hours or high-use seasons.
Fix
Size systems using peak demand and real load profilesânot just average kWh.
â Do this:
Problem
Loads get assumed âON all dayâ when they actually cycle on and off.
Fix
Apply real runtime estimates and operating schedules for each major load group.
â Do this:
Problem
Motors and compressors pull high power at startup and may crash the system.
Fix
Include surge/inrush values when sizing inverters and backup systems.
â Do this:
Problem
Studies forget inverter losses, wiring losses, and battery efficiency.
Fix
Add realistic loss factors so the system performs as expected.
â Do this:
Problem
Teams lump all equipment into one bucket, making results unclear and oversized.
Fix
Group loads by type and importance to build phased backup plans.
â Do this:
Problem
The study relies on guesses instead of actual usage.
Fix
Confirm with metering, data loggers, and site checks.
â Do this:
Problem
The engineering plan doesnât match how the building actually runs.
Fix
Coordinate early with operations, finance, and leadership.
â Do this:
Collaboration & Governance
Effective load studies are built by a teamânot a single spreadsheet. Early engagement improves accuracy, reduces surprises, and increases stakeholder buy-in.
Provide insight into daily operations, constraints, schedules, and what ânormalâ really looks like.
Convert demand data into system requirementsâsizing generation, storage, and electrical infrastructure.
Identify practical load priorities during outages and define what must remain on in island mode.
Ensure technical decisions align with local needs, priorities, and long-term resilience goals.
Early-stage load studies support preliminary planning and feasibility analysis. They rely on available data and assumptions that should be validated as projects advance.