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PD IEC TS 61724 — Photovoltaic System Performance Technical Specification Series (Capacity Evaluation Method and Energy Evaluation Method)
Photovoltaic System Performance — Capacity Evaluation Method and Energy Evaluation Method (2016 Editions)
☀️ PD IEC TS 61724 is the BSI-adopted IEC/TS 61724 series for photovoltaic system performance evaluation. The two parts in this batch are Part 2, which specifies the capacity evaluation method for verifying the generating capacity of a PV plant, and Part 3, which specifies the energy evaluation method for predicting and verifying the energy produced over a given period. Both are handled by BSI Technical Committee GEL/82 (Photovoltaic Energy Systems). As companion methodology documents to IEC 61724-1 (photovoltaic system performance monitoring), they provide a unified, auditable framework for performance guarantees, commissioning, and operational performance-ratio (PR) analysis.
💡 “Grid-connected is not the same as compliant”: whether a PV plant actually delivers its design capacity must be assessed by a unified method. This series normalises test boundary, reference conditions, meteorological inputs, and uncertainty so that owners and EPC contractors evaluate performance on the same basis.
1. Series Composition and Framework 📌
| Standard | Title | IEC Edition | Core Content |
|---|---|---|---|
| PD IEC/TS 61724-2:2016 | Photovoltaic system performance — Part 2: Capacity evaluation method | IEC TS 61724-2 (Ed.1.0, 2016-10) | Test scope/schedule/duration, equipment and procedure, targets and reference conditions, uncertainty |
| PD IEC/TS 61724-3:2016 | Photovoltaic system performance — Part 3: Energy evaluation method | IEC TS 61724-3 (Ed.1.0, 2016-07) | Predicted energy and models, meteorological inputs, model calculations, availability data, uncertainty |
⚠️ This series covers the methodology of performance evaluation and does not replace the monitoring-system installation requirements of IEC 61724-1. A real performance monitoring system should be based on IEC 61724-1 for data acquisition, quality, and indicators, with Parts 2 and 3 added for specific evaluation procedures.
2. Part 2: Capacity Evaluation Method ⚡
Capacity evaluation answers “how much power can this plant produce”. Part 2 sets out an operational testing and evaluation workflow:
- Test scope, schedule and duration: specifies the scenarios, time windows, and durations to cover.
- Equipment and measurements: specifies accuracy and calibration requirements for instruments, data acquisition, and weather stations.
- Documentation of performance targets: defines targets under both “unconstrained” and “constrained” operation, and specifies reference conditions.
- Test boundary alignment: defines the test boundary to align with the intended system boundary, avoiding miscounting off-site losses.
- Temperature and irradiance dependence: defines the model of plant output versus temperature and irradiance, for converting measured data to reference conditions.
- Uncertainty definition: gives the combination method for measurement and model uncertainty, so conclusions carry a confidence interval.
By converting measured output to a common reference condition and comparing it with the performance target, the assessment judges whether plant capacity meets specification and by how much.
3. Part 3: Energy Evaluation Method 📈
Energy evaluation answers “how much energy will this plant produce over a period” — the core of performance-guarantee contracts and yield prediction. Part 3 includes:
- Calculation and documentation of predicted energy: specifies how predicted energy is computed and the model used to calculate expected energy, fixing the calculation route in advance.
- Test boundary alignment: consistent with Part 2, so prediction and system boundaries coincide.
- Meteorological inputs: specifies the meteorological data sources (irradiance, temperature, wind speed) used for prediction and their suitability.
- PV inputs: specifies how PV parameters (nominal power, temperature coefficients, etc.) are taken.
- Model calculations: defines the performance model (e.g., irradiance-temperature model, system efficiency model) and its parameters.
- Identification of availability-related data: identifies and excludes/processes system unavailable periods, so outage losses are not mistaken for poor performance.
- Uncertainty definition: gives an uncertainty range for the predicted energy.
🚨 If energy evaluation does not correctly handle unavailability and outages, grid curtailment and equipment faults are wrongly counted as “low efficiency”, leading to misjudgement. Part 3 requires unavailable periods to be clearly identified and separated to keep the performance assessment fair.
4. Summary of Key Technical Points 🔧
| Topic | Key Content | Document |
|---|---|---|
| Test boundary | Align with intended system boundary | 61724-2 / -3 |
| Reference conditions | Unconstrained/constrained operating targets | 61724-2 |
| Temperature/irradiance correlation | Model of output vs. temperature and irradiance | 61724-2 |
| Predicted energy | Definition of models and meteorological/PV inputs | 61724-3 |
| Unavailability handling | Identify and separate unavailable periods | 61724-3 |
| Uncertainty | Combination of measurement and model uncertainty | 61724-2 / -3 |
5. Engineering Practice 🛠️
- 🔍 Commissioning: perform capacity testing per Part 2 to verify the rated output of a grid-connected plant matches the design value.
- 🔧 Performance guarantee contracts: cite Part 3’s energy evaluation method in EPC or equipment supply contracts as the assessment basis for energy guarantees.
- 📊 Operational performance diagnostics: compare measured energy with predicted values, and use Part 2 conversion methods to locate efficiency losses (module degradation, mismatch, inverter efficiency, soiling).
- 📋 Financing and insurance: use performance data from this series to support project financing, asset valuation, and insurance claims.
6. Frequently Asked Questions ❓
❓ Q: What is the difference between capacity and energy evaluation?
A: Capacity evaluation answers “how much power” — instantaneous/rated output at reference conditions; energy evaluation answers “how much energy” — cumulative energy over a period. The former verifies installed capacity, the latter supports energy guarantees and long-term performance.
A: Capacity evaluation answers “how much power” — instantaneous/rated output at reference conditions; energy evaluation answers “how much energy” — cumulative energy over a period. The former verifies installed capacity, the latter supports energy guarantees and long-term performance.
❓ Q: What are “unconstrained” and “constrained” operation?
A: Unconstrained operation is operation at the system’s own capability without external limits (grid curtailment, derating); constrained operation is subject to such external or internal limits. Performance targets are defined separately so that “system performance” and “external influence” can be distinguished.
A: Unconstrained operation is operation at the system’s own capability without external limits (grid curtailment, derating); constrained operation is subject to such external or internal limits. Performance targets are defined separately so that “system performance” and “external influence” can be distinguished.
❓ Q: Why convert measured data to reference conditions?
A: PV output depends strongly on irradiance and temperature; direct comparison across different weather is meaningless. Converting to a common reference condition (standard irradiance, reference temperature) enables fair comparison, acceptance, and cross-evaluation.
A: PV output depends strongly on irradiance and temperature; direct comparison across different weather is meaningless. Converting to a common reference condition (standard irradiance, reference temperature) enables fair comparison, acceptance, and cross-evaluation.
🔍 Q: Why does uncertainty matter in this series?
A: PV performance testing is affected by weather, instrument accuracy, model error, and more. Giving an uncertainty range lets one judge whether a performance deviation is within tolerance, rather than mistaking measurement noise for real shortfall.
A: PV performance testing is affected by weather, instrument accuracy, model error, and more. Giving an uncertainty range lets one judge whether a performance deviation is within tolerance, rather than mistaking measurement noise for real shortfall.